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 continuous semantic representations of symbolic expressions"
  Abstract: Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

Connected through hyperedge HE-2 (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"
    - "a deep language model for software code"
    - "deep api learning"

Connected through hyperedge HE-3 (induced by source paper "learning to prove theorems via interacting with proof assistants"):
  with members:
    - "holist an environment for machine learning of higher order theorem proving"
    - "code2vec learning distributed representations of code"
    - "gamepad a learning environment for theorem proving"
    - "generative code modeling with graphs"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "end to end differentiable proving"
    - "deep network guided proof search"

Connected through hyperedge HE-4 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-5 (induced by source paper "embedding symbolic knowledge into deep networks"):
  with members:
    - "learning logistic circuits"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-6 (induced by source paper "siamese recurrent networks learn first order logic reasoning and exhibit zero shot compositional generalization"):
  with members:
    - "sentence entailment in compositional distributional semantics"
    - "deep learning a critical appraisal"
    - "can neural networks understand logical entailment"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "learning explanatory rules from noisy data"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "understanding deep learning requires rethinking generalization"
    - "building machines that learn and think like people"

Connected through hyperedge HE-7 (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 to transduce with unbounded memory"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-8 (induced by source paper "analysing mathematical reasoning abilities of neural models"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "relational recurrent neural networks"
    - "can neural networks understand logical entailment"
    - "attention is all you need"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "adaptive computation time for recurrent neural networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "annotating derivations a new evaluation strategy and dataset for algebra word problems"

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)