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: "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
  Abstract: The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory suggests Shapley values as a unique way of assigning relevance scores such that certain desirable properties are satisfied. Unfortunately, the exact evaluation of Shapley values is prohibitively expensive, exponential in the number of input features. In this work, by leveraging recent results on uncertainty propagation, we propose a novel, polynomial-time approximation of Shapley values in deep neural networks. We show that our method produces significantly better approximations of Shapley values than existing state-of-the-art attribution methods.

Connected through hyperedge HE-1 (induced by source paper "fedcoin a peer to peer payment system for federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "towards efficient data valuation based on the shapley value"
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "baffle blockchain based aggregator free federated learning"
    - "federated machine learning concept and applications"
    - "blockchained on device federated learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "majority is not enough bitcoin mining is vulnerable"

Connected through hyperedge HE-2 (induced by source paper "improving feature attribution through input specific network pruning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "a benchmark for interpretability methods in deep neural networks"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"

Connected through hyperedge HE-3 (induced by source paper "analysing neural language models contextual decomposition reveals default reasoning in number and gender assignment"):
  with members:
    - "the emergence of number and syntax units in lstm language models"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "hierarchical interpretations for neural network predictions"
    - "what do rnn language models learn about filler gap dependencies"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "analysis methods in neural language processing a survey"
    - "targeted syntactic evaluation of language models"

Connected through hyperedge HE-4 (induced by source paper "an explanation method for siamese neural networks"):
  with members:
    - "evaluating explainers via perturbation"
    - "alime autoencoder based approach for local interpretability"
    - "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"
    - "interpretable counterfactual explanations guided by prototypes"
    - "normlime a new feature importance metric for explaining deep neural networks"
    - "model agnostic contrastive explanations for structured data"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "explaining models by propagating shapley values of local components"):
  with members:
    - "generalized integrated gradients a practical method for explaining diverse ensembles"
    - "learning explainable models using attribution priors"
    - "the many shapley values for model explanation"
    - "explainable ai for trees from local explanations to global understanding"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "the explanation game explaining machine learning models with cooperative game theory"):
  with members:
    - "the many shapley values for model explanation"
    - "consistent individualized feature attribution for tree ensembles"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

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)