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 to prove with tactics"
  Abstract: We implement a automated tactical prover TacticToe on top of the HOL4 interactive theorem prover. TacticToe learns from human proofs which mathematical technique is suitable in each proof situation. This knowledge is then used in a Monte Carlo tree search algorithm to explore promising tactic-level proof paths. On a single CPU, with a time limit of 60 seconds, TacticToe proves 66.4 percent of the 7164 theorems in HOL4's standard library, whereas E prover with auto-schedule solves 34.5 percent. The success rate rises to 69.0 percent by combining the results of TacticToe and E prover.

Connected through hyperedge HE-1 (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"
    - "enigma efficient learning based inference guiding machine"
    - "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-2 (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-3 (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"
    - "end to end differentiable proving"
    - "deep network guided proof search"
    - "learning to represent programs with graphs"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning in hol4"):
  with members:
    - "hammering mizar by learning clause guidance"
    - "learning a sat solver from single bit supervision"
    - "first experiments with neural translation of informal to formal mathematics"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "tactictoe learning to reason with hol4 tactics"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (induced by source paper "grunge a grand unified atp challenge"):
  with members:
    - "the higher order prover leo iii"
    - "tactictoe learning to reason with hol4 tactics"
    - "encoding monomorphic and polymorphic types"
    - "premise selection and external provers for hol4"
    - "premise selection for mathematics by corpus analysis and kernel methods"
    - "learning assisted automated reasoning with flyspeck"

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)