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: "an empirical evaluation of rule extraction from recurrent neural networks"
  Abstract: Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis. Though already a challenging problem in statistical learning in general, the difficulty is even greater when highly non-linear, recursive models, such as recurrent neural networks (RNNs), are fit to data. Here, we study the extraction of rules from second order recurrent neural networks trained to recognize the Tomita grammars. We show that production rules can be stably extracted from trained RNNs and that in certain cases the rules outperform the trained RNNs.

Connected through hyperedge HE-1 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model"):
  with members:
    - "dire a neural approach to decompiled identifier naming"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"
    - "neural network based graph embedding for cross platform binary code similarity detection"

Connected through hyperedge HE-2 (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"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"
    - "dilated recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-4 (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"
    - "approximating the spectrum of a graph"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-5 (induced by source paper "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "deepproblog neural probabilistic logic programming"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "a simple neural network module for relational reasoning"
    - "tensorlog deep learning meets probabilistic dbs"
    - "logic tensor networks for semantic image interpretation"

Connected through hyperedge HE-6 (induced by source paper "learning causal state representations of partially observable environments"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "deep variational reinforcement learning for pomdps"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "rule extraction algorithm for deep neural networks a review"
    - "deep recurrent q learning for partially observable mdps"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-7 (induced by source paper "measurable counterfactual local explanations for any classifier"):
  with members:
    - "explaining explanations in ai"
    - "contrastive explanation a structural model approach"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "knowledge extraction from the learning of sequences in a long short term memory lstm architecture"):
  with members:
    - "explaining black boxes on sequential data using weighted automata"
    - "lstm a search space odyssey"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "the mythos of model interpretability"
    - "representation learning a review and new perspectives"
    - "the numpy array a structure for efficient numerical computation"

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)