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: "internal guidance for satallax"
  Abstract: We propose a new internal guidance method for automated theorem provers based on the given-clause algorithm. Our method influences the choice of unprocessed clauses using positive and negative examples from previous proofs. To this end, we present an efficient scheme for Naive Bayesian classification by generalising label occurrences to types with monoid structure. This makes it possible to extend existing fast classifiers, which consider only positive examples, with negative ones. We implement the method in the higher-order logic prover Satallax, where we modify the delay with which propositions are processed. We evaluated our method on a simply-typed higher-order logic version of the Flyspeck project, where it solves 26% more problems than Satallax without internal guidance.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "learning to prove with tactics"):
  with members:
    - "tactictoe learning to reason with hol4 tactics"
    - "sepia search for proofs using inferred automata"
    - "recycling proof patterns in coq case studies"
    - "machine learning in proof general interfacing interfaces"
    - "learning assisted automated reasoning with flyspeck"

Connected through hyperedge HE-3 (induced by source paper "premise selection for theorem proving by deep graph embedding"):
  with members:
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "holophrasm a neural automated theorem prover for higher order logic"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-4 (induced by source paper "deep network guided proof search"):
  with members:
    - "holophrasm a neural automated theorem prover for higher order logic"
    - "improved semantic representations from tree structured long short term memory networks"
    - "mizar 40 for mizar 40"
    - "reasoning about entailment with neural attention"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "tactictoe learning to reason with hol4 tactics"):
  with members:
    - "sepia search for proofs using inferred automata"
    - "learning assisted theorem proving with millions of lemmas"
    - "recycling proof patterns in coq case studies"
    - "blistr the blind strategymaker"
    - "machine learning in proof general interfacing interfaces"
    - "learning assisted automated reasoning with flyspeck"

Connected through hyperedge HE-6 (induced by source paper "monte carlo tableau proof search"):
  with members:
    - "leopard a generic platform for the implementation of higher order reasoners"
    - "mizar 40 for mizar 40"
    - "certified connection tableaux proofs for hol light and tptp"
    - "premise selection for mathematics by corpus analysis and kernel methods"
    - "learning assisted automated reasoning with flyspeck"

Connected through hyperedge HE-7 (induced by source paper "holstep a machine learning dataset for higher order logic theorem proving"):
  with members:
    - "end to end memory networks"
    - "premise selection for mathematics by corpus analysis and kernel methods"
    - "males a framework for automatic tuning of automated theorem provers"

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)