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 reason in large theories without imitation"
  Abstract: Automated theorem proving in large theories can be learned via reinforcement learning over an indefinitely growing action space. In order to select actions, one performs nearest neighbor lookups in the knowledge base to find premises to be applied. Here we address the exploration for reinforcement learning in this space. Approaches (like epsilon-greedy strategy) that sample actions uniformly do not scale to this scenario as most actions lead to dead ends and unsuccessful proofs which are not useful for training our models. In this paper, we compare approaches that select premises using randomly initialized similarity measures and mixing them with the proposals of the learned model. We evaluate these on the HOList benchmark for tactics based higher order theorem proving. We implement an automated theorem prover named DeepHOL-Zero that does not use any of the human proofs and show that our improved exploration method manages to expand the training set continuously. DeepHOL-Zero outperforms the best theorem prover trained by imitation learning alone.

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"
    - "how powerful are graph neural networks"
    - "reinforcement learning of theorem proving"
    - "neural message passing for quantum chemistry"

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 "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"
    - "graph representations for higher order logic and theorem proving"
    - "hammering mizar by learning clause guidance"
    - "property invariant embedding for automated reasoning"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "mathematical reasoning in latent space"):
  with members:
    - "can neural networks learn symbolic rewriting"
    - "model based reinforcement learning for atari"
    - "graph representations for higher order logic and theorem proving"
    - "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-5 (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"
    - "graph representations for higher order logic and theorem proving"
    - "on learning to prove"
    - "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)