human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "de randomized smoothing for certifiable defense against patch attacks"):
  Abstract: Patch adversarial attacks on images, in which the attacker can distort pixels within a region of bounded size, are an important threat model since they provide a quantitative model for physical adversarial attacks. In this paper, we introduce a certifiable defense against patch attacks that guarantees for a given image and patch attack size, no patch adversarial examples exist. Our method is related to the broad class of randomized smoothing robustness schemes which provide high-confidence probabilistic robustness certificates. By exploiting the fact that patch attacks are more constrained than general sparse attacks, we derive meaningfully large robustness certificates. Additionally, the algorithm we propose is de-randomized, providing deterministic certificates. To the best of our knowledge, there exists only one prior method for certifiable defense against patch attacks, which relies on interval bound propagation. While this sole existing method performs well on MNIST, it has several limitations: it requires computationally expensive training, does not scale to ImageNet, and performs poorly on CIFAR-10. In contrast, our proposed method effectively addresses all of these issues: our classifier can be trained quickly, achieves high clean and certified robust accuracy on CIFAR-10, and provides certificates at the ImageNet scale. For example, for a 5*5 patch attack on CIFAR-10, our method achieves up to around 57.8% certified accuracy (with a classifier around 83.9% clean accuracy), compared to at most 30.3% certified accuracy for the existing method (with a classifier with around 47.8% clean accuracy), effectively establishing a new state-of-the-art. Code is available at this https URL.

Includes members:
  Member "curse of dimensionality on randomized smoothing for certifiable robustness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "randomized smoothing of all shapes and sizes")
  Member "randomized smoothing of all shapes and sizes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial robustness through local lipschitzness")
  Member "certified robustness to label flipping attacks via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rab provable robustness against backdoor attacks")
  Member "random smoothing might be unable to certify ell_ infty robustness for high dimensional images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial robustness through local lipschitzness")
  Member "certified defenses for adversarial patches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "detecting patch adversarial attacks with image residuals")
  Member "tight certificates of adversarial robustness for randomly smoothed classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "robustness certificates for sparse adversarial attacks by randomized ablation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "randomized smoothing of all shapes and sizes")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

Question: Please tell me which class the source paper 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)