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 heuristics for automated reasoning through deep reinforcement learning"
  Abstract: We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We consider search algorithms for quantified Boolean logics, that already can solve formulas of impressive size - up to 100s of thousands of variables. The main challenge is to find a representation which lends to making predictions in a scalable way. The heuristics learned through our approach significantly improve over the handwritten heuristics for several sets of formulas.

Connected through hyperedge HE-1 (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 to reason in large theories without imitation"
    - "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "graph representations for higher order logic and theorem proving"):
  with members:
    - "neural logic machines"
    - "guiding high performance sat solvers with unsat core predictions"
    - "generating correctness proofs with neural networks"
    - "a comprehensive survey on graph neural networks"
    - "gamepad a learning environment for theorem proving"
    - "relational inductive biases deep learning and graph networks"
    - "can neural networks understand logical entailment"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (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"
    - "graph representations for higher order logic and theorem proving"
    - "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"

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"
    - "learning to reason in large theories without imitation"
    - "using state predictions for value regularization in curiosity driven 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 "graph neural reasoning may fail in certifying boolean unsatisfiability"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "what can neural networks reason about"
    - "graph neural reasoning for 2 quantified boolean formula solvers"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning a sat solver from single bit supervision"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "deep sets"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "exact combinatorial optimization with graph convolutional neural networks"):
  with members:
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "adam a method for stochastic optimization"
    - "dash dynamic approach for switching heuristics"

Connected through hyperedge HE-7 (induced by source paper "coloring big graphs with alphagozero"):
  with members:
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "representation learning on graphs methods and applications"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-8 (induced by source paper "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "gamepad a learning environment for theorem proving"
    - "automated proof synthesis for propositional logic with deep neural networks"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"

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)