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: "graph representations for higher order logic and theorem proving"
  Abstract: This paper presents the first use of graph neural networks (GNNs) for higher-order proof search and demonstrates that GNNs can improve upon state-of-the-art results in this domain. Interactive, higher-order theorem provers allow for the formalization of most mathematical theories and have been shown to pose a significant challenge for deep learning. Higher-order logic is highly expressive and, even though it is well-structured with a clearly defined grammar and semantics, there still remains no well-established method to convert formulas into graph-based representations. In this paper, we consider several graphical representations of higher-order logic and evaluate them against the HOList benchmark for higher-order theorem proving.

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"
    - "holist an environment for machine learning of higher order theorem proving"
    - "a comprehensive survey on graph neural networks"
    - "structured neural summarization"

Connected through hyperedge HE-2 (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"
    - "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"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (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"
    - "learning to speak and act in a fantasy text adventure game"
    - "hammering mizar by learning clause guidance"
    - "obstacle tower a generalization challenge in vision control and planning"

Connected through hyperedge HE-4 (induced by source paper "a deep reinforcement learning based approach to learning transferable proof guidance strategies"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "towards finding longer proofs"
    - "guiding theorem proving by recurrent neural networks"
    - "hammering mizar by learning clause guidance"
    - "property invariant embedding for automated reasoning"
    - "learning to reason in large theories without imitation"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "improving sat solver heuristics with graph networks and reinforcement learning"):
  with members:
    - "reinforcement learning driven heuristic optimization"
    - "guiding high performance sat solvers with unsat core predictions"
    - "learning transferable cooperative behavior in multi agent teams"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"

Connected through hyperedge HE-6 (induced by source paper "improving graph neural network representations of logical formulae with subgraph pooling"):
  with members:
    - "holist an environment for machine learning of higher order 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-7 (induced by source paper "mathematical reasoning in latent space"):
  with members:
    - "can neural networks learn symbolic rewriting"
    - "model based reinforcement learning for atari"
    - "learning to reason in large theories without imitation"
    - "using state predictions for value regularization in curiosity driven deep reinforcement learning"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "recurrent world models facilitate policy evolution"
    - "recurrent environment simulators"

Connected through hyperedge HE-8 (induced by source paper "learning to prove theorems by learning to generate theorems"):
  with members:
    - "learning to prove theorems via interacting with proof assistants"
    - "mathematical reasoning in latent space"
    - "on learning to prove"
    - "learning to reason in large theories without imitation"
    - "reinforcement learning of theorem proving"
    - "learning goal embeddings via self play for hierarchical reinforcement learning"
    - "automatic goal generation for reinforcement learning agents"
    - "intrinsic motivation and automatic curricula via asymmetric self play"

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.LG(Machine Learning)