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: "hammering mizar by learning clause guidance"
  Abstract: We describe a very large improvement of existing hammer-style proof automation over large ITP libraries by combining learning and theorem proving. In particular, we have integrated state-of-the-art machine learners into the E automated theorem prover, and developed methods that allow learning and efficient internal guidance of E over the whole Mizar library. The resulting trained system improves the real-time performance of E on the Mizar library by 70% in a single-strategy setting.

Connected through hyperedge HE-1 (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"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "enigma efficient learning based inference guiding machine"
    - "tactictoe learning to reason with hol4 tactics"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "enigma anonymous symbol independent inference guiding machine system description"):
  with members:
    - "enigmawatch proofwatch meets enigma"
    - "property invariant embedding for automated reasoning"
    - "enigma efficient learning based inference guiding machine"
    - "xgboost a scalable tree boosting system"
    - "sharing hol4 and hol light proof knowledge"
    - "premise selection and external provers for hol4"
    - "mizar 40 for mizar 40"
    - "hol y hammer online atp service for hol light"

Connected through hyperedge HE-3 (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"
    - "obstacle tower a generalization challenge in vision control and planning"

Connected through hyperedge HE-4 (induced by source paper "a deep reinforcement learning based approach to learning transferable proof guidance strategies"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "towards finding longer proofs"
    - "guiding theorem proving by recurrent neural networks"
    - "graph representations for higher order logic and theorem proving"
    - "property invariant embedding for automated reasoning"
    - "learning to reason in large theories without imitation"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "enigmawatch proofwatch meets enigma"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "reinforcement learning of theorem proving"
    - "proofwatch watchlist guidance for large theories in e"
    - "enigma efficient learning based inference guiding machine"
    - "deep network guided proof search"
    - "xgboost a scalable tree boosting system"
    - "internal guidance for satallax"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning in hol4"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "first experiments with neural translation of informal to formal mathematics"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "tactictoe learning to reason with hol4 tactics"
    - "xgboost a scalable tree boosting system"

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)