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: "atpboost learning premise selection in binary setting with atp feedback"
  Abstract: ATPboost is a system for solving sets of large-theory problems by interleaving ATP runs with state-of-the-art machine learning of premise selection from the proofs. Unlike many previous approaches that use multi-label setting, the learning is implemented as binary classification that estimates the pairwise-relevance of (theorem, premise) pairs. ATPboost uses for this the XGBoost gradient boosting algorithm, which is fast and has state-of-the-art performance on many tasks. Learning in the binary setting however requires negative examples, which is nontrivial due to many alternative proofs. We discuss and implement several solutions in the context of the ATP/ML feedback loop, and show that ATPboost with such methods significantly outperforms the k-nearest neighbors multilabel classifier.

Connected through hyperedge HE-1 (induced by source paper "guiding theorem proving by recurrent neural networks"):
  with members:
    - "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"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "enigma ng efficient neural and gradient boosted inference guidance for e"):
  with members:
    - "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"
    - "monte carlo tableau proof search"
    - "deepmath deep sequence models for premise selection"
    - "xgboost a scalable tree boosting system"
    - "premise selection and external provers for hol4"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning of theorem proving"):
  with members:
    - "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"
    - "holophrasm a neural automated theorem prover for higher order logic"
    - "premise selection and external provers for hol4"
    - "premise selection for mathematics by corpus analysis and kernel methods"

Connected through hyperedge HE-5 (induced by source paper "verifying security protocols using dynamic strategies"):
  with members:
    - "formal analysis of 5g authentication"
    - "automated proof synthesis for propositional logic with deep neural networks"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "deepmath deep sequence models for premise selection"
    - "a comprehensive formal security analysis of oauth 2 0"
    - "end to end memory networks"
    - "a hybrid analysis for security protocols with state"

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)