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: "holstep a machine learning dataset for higher order logic theorem proving"
  Abstract: Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of simple baseline machine learning models suited for the tasks (including logistic regression, convolutional neural networks and recurrent neural networks). The results of our baseline models show the promise of applying machine learning to HOL theorem proving.

Connected through hyperedge HE-1 (induced by source paper "elementary logic in linear space"):
  with members:
    - "on learning to prove"
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "reinforcement learning of theorem proving"
    - "deep network guided proof search"
    - "analysis of boolean functions"
    - "machine learning of coq proof guidance first experiments"
    - "probabilities on sentences in an expressive logic"

Connected through hyperedge HE-2 (induced by source paper "towards finding longer proofs"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "meta learning of sequential strategies"
    - "grunge a grand unified atp challenge"
    - "analysing mathematical reasoning abilities of neural models"
    - "graph representations for higher order logic and theorem proving"
    - "learning to speak and act in a fantasy text adventure game"
    - "hammering mizar by learning clause guidance"

Connected through hyperedge HE-3 (induced by source paper "a survey on theorem provers in formal methods"):
  with members:
    - "comparison of two theorem provers isabelle hol and coq"
    - "the boyer moore waterfall model revisited"
    - "reinforcement learning of theorem proving"
    - "qwire practice formal verification of quantum circuits in coq"
    - "proofwatch watchlist guidance for large theories in e"
    - "verifying asymptotic time complexity of imperative programs in isabelle"
    - "formalized lambek calculus in higher order logic hol4"
    - "the x86isa books features usage and future plans"

Connected through hyperedge HE-4 (induced by source paper "guiding high performance sat solvers with unsat core predictions"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to branch"
    - "gamepad a learning environment for theorem proving"
    - "robustfill neural program learning under noisy i o"
    - "ray a distributed framework for emerging ai applications"
    - "schur number five"
    - "deep network guided proof search"
    - "premise selection for theorem proving by deep graph embedding"

Connected through hyperedge HE-5 (induced by source paper "improving graph neural network representations of logical formulae with subgraph pooling"):
  with members:
    - "graph representations for higher order logic and theorem proving"
    - "holist an environment for machine learning of higher order theorem proving"
    - "property invariant embedding for automated reasoning"
    - "gamepad a learning environment for theorem proving"
    - "can neural networks understand logical entailment"
    - "premise selection with neural networks and distributed representation of features"
    - "neural message passing for quantum chemistry"
    - "deep network guided proof search"

Connected through hyperedge HE-6 (induced by source paper "holist an environment for machine learning of higher order theorem proving"):
  with members:
    - "gamepad a learning environment for theorem proving"
    - "reinforcement learning of theorem proving"
    - "deep network guided proof search"
    - "tactictoe learning to reason with hol4 tactics"
    - "premise selection for theorem proving by deep graph embedding"
    - "squad 100 000 questions for machine comprehension of text"
    - "wavenet a generative model for raw audio"
    - "holophrasm a neural automated theorem prover for higher order logic"

Connected through hyperedge HE-7 (induced by source paper "guiding theorem proving by recurrent neural networks"):
  with members:
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "gamepad a learning environment for theorem proving"
    - "first experiments with neural translation of informal to formal mathematics"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "beam search strategies for neural machine translation"
    - "enigma efficient learning based inference guiding machine"
    - "deep network guided proof search"

Connected through hyperedge HE-8 (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"
    - "tactictoe learning to reason with hol4 tactics"
    - "towards proof synthesis guided by neural machine translation for intuitionistic propositional logic"
    - "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.AI(Artificial Intelligence)