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 "provable robust learning based on transformation specific smoothing"):
  Abstract: As machine learning systems become pervasive, safeguarding their security is critical. Recent work has demonstrated that motivated adversaries could manipulate the test data to mislead ML systems to make arbitrary mistakes. So far, most research has focused on providing provable robustness guarantees for a specific $\ell_p$ norm bounded adversarial perturbation. However, in practice there are more adversarial transformations that are realistic and of semantic meaning, requiring to be analyzed and ideally certified. In this paper we aim to provide {\em a unified framework for certifying ML model robustness against general adversarial transformations}. First, we leverage the function smoothing strategy to certify robustness against a series of adversarial transformations such as rotation, translation, Gaussian blur, etc. We then provide sufficient conditions and strategies for certifying certain transformations. For instance, we propose a novel sampling based interpolation approach with the estimated Lipschitz upper bound to certify the robustness against rotation transformation. In addition, we theoretically optimize the smoothing strategies for certifying the robustness of ML models against different transformations. For instance, we show that smoothing by sampling from exponential distribution provides tighter robustness bound than Gaussian. We also prove two generalization gaps for the proposed framework to understand its theoretic barrier. Extensive experiments show that our proposed unified framework significantly outperforms the state-of-the-art certified robustness approaches on several datasets including ImageNet.

Includes members:
  Member "breaking certified defenses semantic adversarial examples with spoofed robustness certificates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the security of randomized defenses against adversarial samples")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "exploiting excessive invariance caused by norm bounded adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on adaptive attacks to adversarial example defenses")
  Member "a convex relaxation barrier to tight robustness verification of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "on the effectiveness of interval bound propagation for training verifiably robust models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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)