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: "interpreting black box predictions using fisher kernels"
  Abstract: Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models. To this end, we take a novel look at black box interpretation of test predictions in terms of training examples. Our goal is to ask `which training examples are most responsible for a given set of predictions'? To answer this question, we make use of Fisher kernels as the defining feature embedding of each data point, combined with Sequential Bayesian Quadrature (SBQ) for efficient selection of examples. In contrast to prior work, our method is able to seamlessly handle any sized subset of test predictions in a principled way. We theoretically analyze our approach, providing novel convergence bounds for SBQ over discrete candidate atoms. Our approach recovers the application of influence functions for interpretability as a special case yielding novel insights from this connection. We also present applications of the proposed approach to three use cases: cleaning training data, fixing mislabeled examples and data summarization.

Connected through hyperedge HE-1 (induced by source paper "deep active learning for biased datasets via fisher kernel self supervision"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "variational adversarial active learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised representation learning by predicting image rotations"
    - "learning to reweight examples for robust deep learning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-2 (induced by source paper "on the in fidelity and sensitivity for explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "certified adversarial robustness via randomized smoothing"
    - "towards robust locally linear deep networks"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "counterfactual visual explanations"
    - "representer point selection for explaining deep neural networks"
    - "model agnostic supervised local explanations"
    - "interpretable deep learning under fire"

Connected through hyperedge HE-3 (induced by source paper "on the accuracy of influence functions for measuring group effects"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "contamination attacks and mitigation in multi party machine learning"
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "understanding the origins of bias in word embeddings"
    - "certified defenses for data poisoning attacks"
    - "a broad coverage challenge corpus for sentence understanding through inference"

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)