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: "extracting automata from recurrent neural networks using queries and counterexamples"
  Abstract: We address the problem of extracting an automaton from a trained recurrent neural network (RNN). We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given RNN. We use Angluin's L* algorithm as a learner and the given RNN as an oracle, employing abstract interpretation of the RNN for answering equivalence queries. Our technique allows automaton-extraction from the RNN while avoiding state explosion, even when the state vectors are large and fine differentiation is required between RNN states. #R##N#We experiment with automata extraction from multi-layer GRU and LSTM based RNNs, with state-vector dimensions and underlying automata and alphabet sizes which are significantly larger than in previous automata-extraction work. In some cases, the underlying target language can be described with a succinct automata, yet the extracted automata is large and complex. These are cases in which the RNN failed to learn the intended generalization, and our extraction procedure highlights words which are misclassified by the seemingly "perfect" RNN.

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"
    - "representing formal languages a comparison between finite automata and recurrent neural networks"
    - "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 "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"

Connected through hyperedge HE-3 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-4 (induced by source paper "discovering the compositional structure of vector representations with role learning networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "correlating neural and symbolic representations of language"
    - "measuring compositionality in representation learning"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "analyzing machine learned representations a natural language case study"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-5 (induced by source paper "analyzing recurrent neural network by probabilistic abstraction"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "explaining black boxes on sequential data using weighted automata"
    - "learning with interpretable structure from rnn"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "safety and trustworthiness of deep neural networks a survey"
    - "an empirical evaluation of rule extraction from recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a2 extracting cyclic switchings from dob nets for rejecting excessive disturbances"):
  with members:
    - "dob net actively rejecting unknown excessive time varying disturbances"
    - "learning finite state representations of recurrent policy networks"
    - "particle filter networks with application to visual localization"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "deepnav learning to navigate large cities"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous deep q learning with model based acceleration"
    - "q prop sample efficient policy gradient with an off policy critic"

Connected through hyperedge HE-7 (induced by source paper "shapley homology topological analysis of sample influence for neural networks"):
  with members:
    - "dataset distillation"
    - "adversarial attack on graph structured data"
    - "verification of recurrent neural networks through rule extraction"
    - "representer point selection for explaining deep neural networks"
    - "learning to reweight examples for robust deep learning"
    - "data poisoning attacks against online learning"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "approximating the spectrum of a graph"

Connected through hyperedge HE-8 (induced by source paper "weighted automata extraction from recurrent neural networks via regression on state spaces"):
  with members:
    - "state regularized recurrent neural networks"
    - "intrusion prediction with system call sequence to sequence model"
    - "explaining black boxes on sequential data using weighted automata"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "bisimulation metrics for weighted automata"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "advances in all neural speech recognition"

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)