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: "representing formal languages a comparison between finite automata and recurrent neural networks"
  Abstract: We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of the minimal deterministic finite automaton (MDFA) for the language. Our experiments show that such a decoding function indeed exists, and that it maps states of the RNN not to MDFA states, but to states of an {\em abstraction} obtained by clustering small sets of MDFA states into "superstates". A qualitative analysis reveals that the abstraction often has a simple interpretation. Overall, the results suggest a strong structural relationship between internal representations used by RNNs and finite automata, and explain the well-known ability of RNNs to recognize formal grammatical structure.

Connected through hyperedge HE-1 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints"):
  with members:
    - "interpreting and evaluating neural network robustness"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "qualitatively characterizing neural network optimization problems"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "weight annotation in information extraction"):
  with members:
    - "efficient logspace classes for enumeration counting and uniform generation"
    - "complexity bounds for relational algebra over document spanners"
    - "constant delay enumeration for nondeterministic document spanners"
    - "constant delay algorithms for regular document spanners"
    - "document spanners for extracting incomplete information expressiveness and complexity"
    - "snorkel rapid training data creation with weak supervision"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "joining extractions of regular expressions"

Connected through hyperedge HE-3 (induced by source paper "induction of subgoal automata for reinforcement learning"):
  with members:
    - "joint inference of reward machines and policies for reinforcement learning"
    - "semantic rl with action grammars data efficient learning of hierarchical task abstractions"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "iterative learning of answer set programs from context dependent examples"
    - "symmetry breaking answer set solving"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)