Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Use of Deep Learning for Symbolic Integration: A Review of (Lample
  and Charton, 2019)   Lample and Charton (2019) describe a system that uses deep learning
technology to compute symbolic, indefinite integrals, and to find symbolic
solutions to first- and second-order ordinary differential equations, when the
solutions are elementary functions. They found that, over a particular test
set, the system could find solutions more successfully than sophisticated
packages for symbolic mathematics such as Mathematica run with a long time-out.
This is an impressive accomplishment, as far as it goes. However, the system
can handle only a quite limited subset of the problems that Mathematica deals
with, and the test set has significant built-in biases. Therefore the claim
that this outperforms Mathematica on symbolic integration needs to be very much
qualified.

3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
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The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int