human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning to discover efficient mathematical identities"
  Abstract: In this paper we explore how machine learning techniques can be applied to the discovery of efficient mathematical identities. We introduce an attribute grammar framework for representing symbolic expressions. Given a set of grammar rules we build trees that combine different rules, looking for branches which yield compositions that are analytically equivalent to a target expression, but of lower computational complexity. However, as the size of the trees grows exponentially with the complexity of the target expression, brute force search is impractical for all but the simplest of expressions. Consequently, we introduce two novel learning approaches that are able to learn from simpler expressions to guide the tree search. The first of these is a simple n-gram model, the other being a recursive neural-network. We show how these approaches enable us to derive complex identities, beyond reach of brute-force search, or human derivation.

Connected through hyperedge HE-1 (induced by source paper "improving the robustness to data inconsistency between training and testing for code completion by hierarchical language model"):
  with members:
    - "program language translation using a grammar driven tree to tree model"
    - "mapping language to code in programmatic context"
    - "code completion with neural attention and pointer networks"
    - "tree to tree neural networks for program translation"
    - "a syntactic neural model for general purpose code generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "learning continuous semantic representations of symbolic expressions"
    - "a deep language model for software code"

Connected through hyperedge HE-2 (induced by source paper "memory augmented recursive neural networks"):
  with members:
    - "can neural networks understand logical entailment"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "graph memory networks for molecular activity prediction"
    - "combining symbolic expressions and black box function evaluations in neural programs"
    - "making neural programming architectures generalize via recursion"
    - "tree memory networks for modelling long term temporal dependencies"
    - "learning continuous semantic representations of symbolic expressions"
    - "learning to transduce with unbounded memory"

Connected through hyperedge HE-3 (induced by source paper "improve language modelling for code completion through statement level language model based on statement embedding generated by bilstm"):
  with members:
    - "program language translation using a grammar driven tree to tree model"
    - "mapping language to code in programmatic context"
    - "tree to tree neural networks for program translation"
    - "a syntactic neural model for general purpose code generation"
    - "learning continuous semantic representations of symbolic expressions"
    - "a deep language model for software code"
    - "deep api learning"
    - "a convolutional attention network for extreme summarization of source code"

Connected through hyperedge HE-4 (induced by source paper "deep learning for symbolic mathematics"):
  with members:
    - "analysing mathematical reasoning abilities of neural models"
    - "neural arithmetic logic units"
    - "learning to parse and translate improves neural machine translation"
    - "attention is all you need"
    - "deep network guided proof search"
    - "learning continuous semantic representations of symbolic expressions"
    - "recurrent neural network grammars"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-5 (induced by source paper "can neural networks understand logical entailment"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "learning continuous semantic representations of symbolic expressions"
    - "squad 100 000 questions for machine comprehension of text"
    - "latent predictor networks for code generation"
    - "learning to navigate in complex environments"
    - "neuro symbolic program synthesis"
    - "better computer go player with neural network and long term prediction"

Connected through hyperedge HE-6 (induced by source paper "open vocabulary learning on source code with a graph structured cache"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "a general path based representation for predicting program properties"
    - "generative code modeling with graphs"
    - "code completion with neural attention and pointer networks"
    - "relational inductive biases deep learning and graph networks"
    - "tree to tree neural networks for program translation"
    - "graph memory networks for molecular activity prediction"
    - "object oriented neural programming oonp for document understanding"

Connected through hyperedge HE-7 (induced by source paper "learning to perform local rewriting for combinatorial optimization"):
  with members:
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "learning to optimize tensor programs"
    - "can neural networks understand logical entailment"
    - "learning of human like algebraic reasoning using deep feedforward neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "reinforcement learning with deep energy based policies"
    - "deep reinforcement learning for multi resource multi machine job scheduling"

Connected through hyperedge HE-8 (induced by source paper "attending to mathematical language with transformers"):
  with members:
    - "universal transformers"
    - "tensor2tensor for neural machine translation"
    - "extensions and limitations of the neural gpu"
    - "attention is all you need"
    - "improving the neural gpu architecture for algorithm learning"
    - "adaptive computation time for recurrent neural networks"
    - "grid long short term memory"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)