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: "certified defenses for adversarial patches"
  Abstract: Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against patch attacks. We begin with a set of experiments showing that most existing defenses, which work by pre-processing input images to mitigate adversarial patches, are easily broken by simple white-box adversaries. Motivated by this finding, we propose the first certified defense against patch attacks, and propose faster methods for its training. Furthermore, we experiment with different patch shapes for testing, obtaining surprisingly good robustness transfer across shapes, and present preliminary results on certified defense against sparse attacks. Our complete implementation can be found on: this https URL.

Connected through hyperedge HE-1 (induced by source paper "de randomized smoothing for certifiable defense against patch attacks"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "randomized smoothing of all shapes and sizes"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"
    - "certifiably robust interpretation in deep learning"

Connected through hyperedge HE-2 (induced by source paper "detecting patch adversarial attacks with image residuals"):
  with members:
    - "de randomized smoothing for certifiable defense against patch attacks"
    - "one pixel attack for fooling deep neural networks"
    - "a fourier perspective on model robustness in computer vision"
    - "adversarial patch"
    - "sentinet detecting physical attacks against deep learning systems"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lavan localized and visible adversarial noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "curse of dimensionality on randomized smoothing for certifiable robustness"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "certified adversarial robustness with additive noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "adversarial and clean data are not twins"
    - "countering adversarial images using input transformations"

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)