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: "enigma efficient learning based inference guiding machine"
  Abstract: ENIGMA is a learning-based method for guiding given clause selection in saturation-based theorem provers. Clauses from many proof searches are classified as positive and negative based on their participation in the proofs. An efficient classification model is trained on this data, using fast feature-based characterization of the clauses . The learned model is then tightly linked with the core prover and used as a basis of a new parameterized evaluation heuristic that provides fast ranking of all generated clauses. The approach is evaluated on the E prover and the CASC 2016 AIM benchmark, showing a large increase of E's performance.

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 "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"
    - "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-3 (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"
    - "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-4 (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-5 (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"
    - "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 "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"
    - "deep network guided proof search"
    - "xgboost a scalable tree boosting system"
    - "internal guidance for satallax"

Connected through hyperedge HE-7 (induced by source paper "enigma ng efficient neural and gradient boosted inference guidance for e"):
  with members:
    - "atpboost learning premise selection in binary setting with atp feedback"
    - "reinforcement learning of theorem proving"
    - "proofwatch watchlist guidance for large theories in e"
    - "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-8 (induced by source paper "hammering mizar by learning clause guidance"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "reinforcement learning of theorem proving"
    - "proofwatch watchlist guidance for large theories in e"
    - "monte carlo tableau proof search"
    - "deepmath deep sequence models for premise selection"
    - "xgboost a scalable tree boosting system"
    - "mizar 40 for mizar 40"
    - "sepia search for proofs using inferred automata"

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.LO(Logic in Computer Science)