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 "smooth adversarial examples"):
  Abstract: This paper investigates the visual quality of the adver-sarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artefacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally smooth on the flat areas of the input image, but it may be noisy on its textured areas and sharp across its edges. This operation relies on Laplacian smoothing, well-known in graph signal processing, which we integrate in the attack pipeline. We benchmark several attacks with and without smoothing under a white-box scenario and evaluate their transferability. Despite the additional constraint of smoothness, our attack has the same probability of success at lower distortion.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "harmonic adversarial attack method":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attacking optical character recognition ocr systems with adversarial watermarks")
  Member "on the suitability of l_p norms for creating and preventing adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "a closed form solution to photorealistic image stylization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning texture invariant representation for domain adaptation of semantic segmentation")
  Member "a flexible convolutional solver with application to photorealistic style transfer":
    also connected through other hyperedges:
      (none)
  Member "low frequency adversarial perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of black box adversarial attacks on computer vision models")
  Member "learning random walk label propagation for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised salient object detection via scribble annotations")

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.CV(Computer Vision and Pattern Recognition)