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 assisted automated reasoning with flyspeck"
  Abstract: The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.

Connected through hyperedge HE-1 (induced by source paper "an experimental study of formula embeddings for automated theorem proving in first order logic"):
  with members:
    - "proof simplification and automated theorem proving"
    - "a deep reinforcement learning based approach to learning transferable proof guidance strategies"
    - "improving graph neural network representations of logical formulae with subgraph pooling"
    - "graph representations for higher order logic and theorem proving"
    - "property invariant embedding for automated reasoning"
    - "learning to reason in large theories without imitation"
    - "how powerful are graph neural networks"
    - "reinforcement learning of theorem proving"

Connected through hyperedge HE-2 (induced by source paper "enigma anonymous symbol independent inference guiding machine system description"):
  with members:
    - "enigmawatch proofwatch meets enigma"
    - "hammering mizar by learning clause guidance"
    - "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"

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"
    - "hammering mizar by learning clause guidance"

Connected through hyperedge HE-4 (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-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"
    - "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"

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 "enigmawatch proofwatch meets enigma"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "hammering mizar by learning clause guidance"
    - "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"

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)