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: "representer point selection for explaining deep neural networks"
  Abstract: We propose to explain the predictions of a deep neural network, by pointing to the set of what we call representer points in the training set, for a given test point prediction. Specifically, we show that we can decompose the pre-activation prediction of a neural network into a linear combination of activations of training points, with the weights corresponding to what we call representer values, which thus capture the importance of that training point on the learned parameters of the network. But it provides a deeper understanding of the network than simply training point influence: with positive representer values corresponding to excitatory training points, and negative values corresponding to inhibitory points, which as we show provides considerably more insight. Our method is also much more scalable, allowing for real-time feedback in a manner not feasible with influence functions.

Connected through hyperedge HE-1 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "on defending against label flipping attacks on malware detection systems"
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (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"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to generate reviews and discovering sentiment"
    - "l shapley and c shapley efficient model interpretation for structured data"

Connected through hyperedge HE-3 (induced by source paper "graphlime local interpretable model explanations for graph neural networks"):
  with members:
    - "gnnexplainer generating explanations for graph neural networks"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "interpretable explorable approximations of black box models"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-4 (induced by source paper "estimating training data influence by tracking gradient descent"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "character level convolutional networks for text classification"
    - "deep residual learning for image recognition"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-5 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based dropout layer for weakly supervised object localization"
    - "robust attribution regularization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "harnessing the vulnerability of latent layers in adversarially trained models"

Connected through hyperedge HE-7 (induced by source paper "gnn explainer a tool for post hoc explanation of graph neural networks"):
  with members:
    - "position aware graph neural networks"
    - "explaine an approach for explaining network embedding based link predictions"
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-8 (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"
    - "interpreting black box predictions using fisher kernels"
    - "model agnostic supervised local explanations"
    - "interpretable deep learning under fire"

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)