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: "adversarial examples for non parametric methods attacks defenses and large sample limits"
  Abstract: Adversarial examples have received a great deal of recent attention because of their potential to uncover security flaws in machine learning systems. However, most prior work on adversarial examples has been on parametric classifiers, for which generic attack and defense methods are known; non-parametric methods have been only considered on an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric methods. We first provide a general region-based attack that applies to a wide range of classifiers, including nearest neighbors, decision trees, and random forests. Motivated by the close connection between non-parametric methods and the Bayes Optimal classifier, we next exhibit a robust analogue to the Bayes Optimal, and we use it to motivate a novel and generic defense that we call adversarial pruning. We empirically show that the region-based attack and adversarial pruning defense are either better than or competitive with existing attacks and defenses for non-parametric methods, while being considerably more generally applicable.

Connected through hyperedge HE-1 (induced by source paper "when are non parametric methods robust"):
  with members:
    - "universal bayes consistency in metric spaces"
    - "adversarially robust submodular maximization under knapsack constraints"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "adversarially robust generalization requires more data"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "defending against adversarial examples with k nearest neighbor"
    - "fast rates for a knn classifier robust to unknown asymmetric label noise"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "advknn adversarial attacks on k nearest neighbor classifiers with approximate gradients"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "harnessing the vulnerability of latent layers in adversarially trained models"
    - "defending against adversarial examples with k nearest neighbor"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "countering adversarial images using input transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "mask r cnn"

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)