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 "are perceptually aligned gradients a general property of robust classifiers"):
  Abstract: For a standard convolutional neural network, optimizing over the input pixels to maximize the score of some target class will generally produce a grainy-looking version of the original image. However, researchers have demonstrated that for adversarially-trained neural networks, this optimization produces images that uncannily resemble the target class. In this paper, we show that these "perceptually-aligned gradients" also occur under randomized smoothing, an alternative means of constructing adversarially-robust classifiers. Our finding suggests that perceptually-aligned gradients may be a general property of robust classifiers, rather than a specific property of adversarially-trained neural networks. We hope that our results will inspire research aimed at explaining this link between perceptually-aligned gradients and adversarial robustness.

Includes members:
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "certifiably robust interpretation in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust saliency maps with decoy enhanced saliency score")
  Member "defending against whitebox adversarial attacks via randomized discretization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an empirical evaluation of perturbation based 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 "theoretical evidence for adversarial robustness through randomization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "randomization matters how to defend against strong adversarial attacks")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "towards robust neural networks via random self ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "mitigating evasion attacks to deep neural networks via region based classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")

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)