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: "holophrasm a neural automated theorem prover for higher order logic"
  Abstract: I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features. Holophrasm exploits the formalism of the Metamath language and explores partial proof trees using a neural-network-augmented bandit algorithm and a sequence-to-sequence model for action enumeration. The system proves 14% of its test theorems from Metamath's set.mm module.

Connected through hyperedge HE-1 (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"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning to prove theorems by learning to generate theorems"):
  with members:
    - "learning to prove theorems via interacting with proof assistants"
    - "mathematical reasoning in latent space"
    - "graph representations for higher order logic and theorem proving"
    - "on learning to prove"
    - "learning to reason in large theories without imitation"
    - "reinforcement learning of theorem proving"
    - "learning goal embeddings via self play for hierarchical reinforcement learning"
    - "automatic goal generation for reinforcement learning agents"

Connected through hyperedge HE-4 (induced by source paper "learning to reason in large theories without imitation"):
  with members:
    - "reinforcement learning of theorem proving"
    - "premise selection for theorem proving by deep graph embedding"
    - "deepmath deep sequence models for premise selection"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"
    - "learning assisted theorem proving with millions of lemmas"
    - "premise selection for mathematics by corpus analysis and kernel methods"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "gamepad a learning environment for theorem proving"):
  with members:
    - "pamper proof method recommendation system for isabelle hol"
    - "learning a sat solver from single bit supervision"
    - "reinforcement learning of theorem proving"
    - "deep learning with dynamic computation graphs"
    - "end to end differentiable proving"
    - "regularizing and optimizing lstm language models"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-6 (induced by source paper "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "automated proof synthesis for propositional logic with deep neural networks"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep network guided proof search"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning of theorem proving"):
  with members:
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep network guided proof search"
    - "tactictoe learning to reason with hol4 tactics"
    - "deepmath deep sequence models for premise selection"
    - "xgboost a scalable tree boosting system"
    - "premise selection and external provers for hol4"
    - "premise selection for mathematics by corpus analysis and kernel methods"

Connected through hyperedge HE-8 (induced by source paper "premise selection with neural networks and distributed representation of features"):
  with members:
    - "learning of human like algebraic reasoning using deep feedforward neural networks"
    - "premise selection for theorem proving by deep graph embedding"
    - "deepmath deep sequence models for premise selection"
    - "mizar 40 for mizar 40"
    - "adam a method for stochastic optimization"

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)