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: "on the robustness of deep k nearest neighbors"
  Abstract: Despite a large amount of attention on adversarial examples, very few works have demonstrated an effective defense against this threat. We examine Deep k-Nearest Neighbor (DkNN), a proposed defense that combines k-Nearest Neighbor (kNN) and deep learning to improve the model's robustness to adversarial examples. It is challenging to evaluate the robustness of this scheme due to a lack of efficient algorithm for attacking kNN classifiers with large k and high-dimensional data. We propose a heuristic attack that allows us to use gradient descent to find adversarial examples for kNN classifiers, and then apply it to attack the DkNN defense as well. Results suggest that our attack is moderately stronger than any naive attack on kNN and significantly outperforms other attacks on DkNN.

Connected through hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "robust decision trees against adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-2 (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"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "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-3 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "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-4 (induced by source paper "protoattend attention based prototypical learning"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representer point selection for explaining deep neural networks"
    - "this looks like that deep learning for interpretable image recognition"
    - "deep anomaly detection with outlier exposure"
    - "catboost gradient boosting with categorical features support"
    - "incremental few shot learning with attention attractor networks"
    - "attention based ensemble for deep metric learning"
    - "bayesian neural networks"

Connected through hyperedge HE-5 (induced by source paper "detection of face recognition adversarial attacks"):
  with members:
    - "adversarial image translation unrestricted adversarial examples in face recognition systems"
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "on adversarial patches real world attack on arcface 100 face recognition system"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "deep face recognition a survey"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "picture it in your mind generating high level visual representations from textual descriptions"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-6 (induced by source paper "advknn adversarial attacks on k nearest neighbor classifiers with approximate gradients"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "evaluating the robustness of nearest neighbor classifiers a primal dual perspective"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "a dual approach to scalable verification of deep networks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "efficient neural network robustness certification with general activation functions"
    - "efficient formal safety analysis of neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial examples for non parametric methods attacks defenses and large sample limits"):
  with members:
    - "robustness certificates against adversarial examples for relu networks"
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "robust decision trees against adversarial examples"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "formal verification of cnn based perception systems"
    - "improving the generalization of adversarial training with domain adaptation"
    - "provable robustness of relu networks via maximization of linear regions"

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.CR(Cryptography and Security)