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: "l shapley and c shapley efficient model interpretation for structured data"
  Abstract: We study instancewise feature importance scoring as a method for model interpretation. Any such method yields, for each predicted instance, a vector of importance scores associated with the feature vector. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions of this kind, but incur an exponential complexity in the number of features. This combinatorial explosion arises from the definition of the Shapley value and prevents these methods from being scalable to large data sets and complex models. We focus on settings in which the data have a graph structure, and the contribution of features to the target variable is well-approximated by a graph-structured factorization. In such settings, we develop two algorithms with linear complexity for instancewise feature importance scoring. We establish the relationship of our methods to the Shapley value and another closely related concept known as the Myerson value from cooperative game theory. We demonstrate on both language and image data that our algorithms compare favorably with other methods for model interpretation.

Connected through hyperedge HE-1 (induced by source paper "on completeness aware concept based explanations in deep neural networks"):
  with members:
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "attention based prototypical learning towards interpretable confident and robust deep neural networks"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "representer point selection for explaining deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-2 (induced by source paper "a distributional framework for data valuation"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "making ai forget you data deletion in machine learning"
    - "who s responsible jointly quantifying the contribution of the learning algorithm and training data"
    - "a marketplace for data an algorithmic solution"
    - "a unified approach to interpreting model predictions"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "ml loo detecting adversarial examples with feature attribution"):
  with members:
    - "on the in fidelity and sensitivity for explanations"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "training verified learners with learned verifiers"
    - "rob gan generator discriminator and adversarial attacker"
    - "adversarial learning and explainability in structured datasets"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "concise and stable explanations using adversarial training"

Connected through hyperedge HE-4 (induced by source paper "explainable machine learning in deployment"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "transparency motivations and challenges"
    - "automatic model monitoring for data streams"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "explaining explanations in ai"

Connected through hyperedge HE-5 (induced by source paper "towards a unified evaluation of explanation methods without ground truth"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluating explainers via perturbation"
    - "can i trust the explainer verifying post hoc explanatory methods"
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "evaluating recurrent neural network explanations"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards robust interpretability with self explaining neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards hierarchical importance attribution explaining compositional semantics for neural sequence models"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical interpretations for neural network predictions"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "a survey of evaluation methods and measures for interpretable machine learning"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "a game theoretic approach to class wise selective rationalization"):
  with members:
    - "functional transparency for structured data a game theoretic approach"
    - "towards robust locally linear deep networks"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "towards robust interpretability with self explaining neural networks"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deriving machine attention from human rationales"
    - "inferring and executing programs for visual reasoning"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-8 (induced by source paper "rethinking cooperative rationalization introspective extraction and complement control"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "interpretable neural predictions with differentiable binary variables"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "game theoretic interpretability for temporal modeling"
    - "towards robust interpretability with self explaining neural networks"
    - "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "inferring and executing programs for visual reasoning"

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)