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: "holist an environment for machine learning of higher order theorem proving"
  Abstract: We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting, open-ended challenge for deep learning. We provide an open-source framework based on the HOL Light theorem prover that can be used as a reinforcement learning environment. HOL Light comes with a broad coverage of basic mathematical theorems on calculus and the formal proof of the Kepler conjecture, from which we derive a challenging benchmark for automated reasoning. We also present a deep reinforcement learning driven automated theorem prover, DeepHOL, with strong initial results on this benchmark.

Connected through hyperedge HE-1 (induced by source paper "teaching temporal logics to neural networks"):
  with members:
    - "mathematical reasoning in latent space"
    - "guiding high performance sat solvers with unsat core predictions"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "analysing mathematical reasoning abilities of neural models"
    - "deep learning for symbolic mathematics"
    - "graph representations for higher order logic and theorem proving"
    - "a comprehensive survey on graph neural networks"
    - "structured neural summarization"

Connected through hyperedge HE-2 (induced by source paper "learning to prove theorems via interacting with proof assistants"):
  with members:
    - "code2vec learning distributed representations of code"
    - "gamepad a learning environment for theorem proving"
    - "generative code modeling with graphs"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "end to end differentiable proving"
    - "deep network guided proof search"
    - "learning to represent programs with graphs"

Connected through hyperedge HE-3 (induced by source paper "improving graph neural network representations of logical formulae with subgraph pooling"):
  with members:
    - "graph representations for higher order logic and theorem proving"
    - "property invariant embedding for automated reasoning"
    - "gamepad a learning environment for theorem proving"
    - "can neural networks understand logical entailment"
    - "premise selection with neural networks and distributed representation of features"
    - "neural message passing for quantum chemistry"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "generating correctness proofs with neural networks"):
  with members:
    - "learning to prove theorems via interacting with proof assistants"
    - "gamepad a learning environment for theorem proving"
    - "a survey of machine learning for big code and naturalness"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "tactictoe learning to reason with hol4 tactics"
    - "towards proof synthesis guided by neural machine translation for intuitionistic propositional logic"
    - "deepmath deep sequence models for premise selection"

Connected through hyperedge HE-5 (induced by source paper "learning dynamic polynomial proofs"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "gamepad a learning environment for theorem proving"
    - "reinforcement learning of theorem proving"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "playing atari with deep reinforcement learning"
    - "control design along trajectories with sums of squares programming"

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)