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: "towards proof synthesis guided by neural machine translation for intuitionistic propositional logic"
  Abstract: Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics. This paper is the first step towards this goal; we propose a proof-synthesis method for the negation-free propositional logic in which we use a DNN to obtain a guide of proof search. The idea is to view the proof-synthesis problem as a translation from a proposition to its proof. We train seq2seq, which is a popular network in neural machine translation, so that it generates a proof encoded as a $\lambda$-term of a given proposition. We implement the whole framework and empirically observe that a generated proof term is close to a correct proof in terms of the tree edit distance of AST. This observation justifies using the output from a trained seq2seq model as a guide for proof search.

Connected through hyperedge HE-1 (induced by source paper "generating correctness proofs with neural networks"):
  with members:
    - "learning to prove theorems via interacting with proof assistants"
    - "holist an environment for machine learning of higher order theorem proving"
    - "gamepad a learning environment for theorem proving"
    - "a survey of machine learning for big code and naturalness"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "tactictoe learning to reason with hol4 tactics"
    - "deepmath deep sequence models for premise selection"

Connected through hyperedge HE-2 (induced by source paper "automated proof synthesis for propositional logic with deep neural networks"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "can neural networks understand logical entailment"
    - "graph neural networks and boolean satisfiability"
    - "exploring generalization in deep learning"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "a syntactic neural model for general purpose code generation"
    - "premise selection for theorem proving by deep graph embedding"
    - "deepmath deep sequence models for premise selection"

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.PL(Programming Languages)