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: "tactictoe learning to reason with hol4 tactics"
  Abstract: Techniques combining machine learning with translation to automated reasoning have recently become an important component of formal proof assistants. Such "hammer" tech- niques complement traditional proof assistant automation as implemented by tactics and decision procedures. In this paper we present a unified proof assistant automation approach which attempts to automate the selection of appropriate tactics and tactic-sequences com- bined with an optimized small-scale hammering approach. We implement the technique as a tactic-level automation for HOL4: TacticToe. It implements a modified A*-algorithm directly in HOL4 that explores different tactic-level proof paths, guiding their selection by learning from a large number of previous tactic-level proofs. Unlike the existing hammer methods, TacticToe avoids translation to FOL, working directly on the HOL level. By combining tactic prediction and premise selection, TacticToe is able to re-prove 39 percent of 7902 HOL4 theorems in 5 seconds whereas the best single HOL(y)Hammer strategy solves 32 percent in the same amount of time.

Connected through hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "guiding high performance sat solvers with unsat core predictions"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "deep learning for symbolic mathematics"
    - "graph representations for higher order logic and theorem proving"
    - "holist an environment for machine learning of higher order theorem proving"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for synthesizing functions in higher order logic"):
  with members:
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "cls smt bringing together combinatory logic synthesis and satisfiability modulo theories"
    - "hammering mizar by learning clause guidance"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "enigma efficient learning based inference guiding machine"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "smart induction for isabelle hol system description"):
  with members:
    - "lifter language to encode induction heuristics for isabelle hol"
    - "towards evolutionary theorem proving for isabelle hol"
    - "pamper proof method recommendation system for isabelle hol"
    - "goal oriented conjecturing for isabelle hol"
    - "a proof strategy language and proof script generation for isabelle hol"

Connected through hyperedge HE-4 (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-5 (induced by source paper "graph representations for higher order logic and theorem proving"):
  with members:
    - "neural logic machines"
    - "guiding high performance sat solvers with unsat core predictions"
    - "generating correctness proofs with neural networks"
    - "a comprehensive survey on graph neural networks"
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "can neural networks understand logical entailment"

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"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "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 "mathematical reasoning in latent space"):
  with members:
    - "can neural networks learn symbolic rewriting"
    - "model based reinforcement learning for atari"
    - "graph representations for higher order logic and theorem proving"
    - "learning to reason in large theories without imitation"
    - "using state predictions for value regularization in curiosity driven deep reinforcement learning"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "recurrent world models facilitate policy evolution"

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"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "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)