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: "deepmath deep sequence models for premise selection"
  Abstract: We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the hand-engineered features of existing state-of-the-art models. To our knowledge, this is the first time deep learning has been applied to theorem proving on a large scale.

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"
    - "learning to reason in large theories without imitation"
    - "how powerful are graph neural networks"
    - "reinforcement learning of theorem proving"

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 "comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition"):
  with members:
    - "using machine learning to improve cylindrical algebraic decomposition"
    - "theoryguru a mathematica package to apply quantifier elimination technology to economics"
    - "non linear real arithmetic benchmarks derived from automated reasoning in economics"
    - "machine learning for mathematical software"
    - "symbolic versus numerical computation and visualization of parameter regions for multistationarity of biological networks"
    - "a case study on the parametric occurrence of multiple steady states"
    - "using machine learning to decide when to precondition cylindrical algebraic decomposition with groebner bases"
    - "truth table invariant cylindrical algebraic decomposition"

Connected through hyperedge HE-4 (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"
    - "learning to prove with tactics"
    - "end to end differentiable proving"
    - "deep network guided proof search"

Connected through hyperedge HE-5 (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"
    - "learning heuristics for automated reasoning through deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "guiding high performance sat solvers with unsat core predictions"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to branch"
    - "gamepad a learning environment for theorem proving"
    - "robustfill neural program learning under noisy i o"
    - "ray a distributed framework for emerging ai applications"
    - "schur number five"
    - "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 "improving graph neural network representations of logical formulae with subgraph pooling"):
  with members:
    - "graph representations for higher order logic and theorem proving"
    - "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"

Connected through hyperedge HE-8 (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"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "recurrent world models facilitate policy evolution"

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)