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 "towards large yet imperceptible adversarial image perturbations with perceptual color distance"):
  Abstract: The success of image perturbations that are designed to fool image classification is assessed in terms of both adversarial effect and visual imperceptibility. In this work, we investigate the contribution of human color perception to perturbations that are not noticeable. Our basic insight is that perceptual color distance makes it possible to drop the conventional assumption that imperceptible perturbations should strive for small $L_p$ norms in RGB space. Our first approach, Perceptual Color distance C&W (PerC-C&W), extends the widely-used C&W approach and produces larger RGB perturbations. PerC-C&W is able to maintain adversarial strength, while contributing to imperceptibility. Our second approach, Perceptual Color distance Alternating Loss (PerC-AL), achieves the same outcome, but does so more efficiently by alternating between the classification loss and perceptual color difference when updating perturbations. Experimental evaluation shows PerC approaches improve robustness and transferability of perturbations over conventional approaches and also demonstrates that the PerC distance can provide added value on top of existing structure-based approaches to creating image perturbations.

Includes members:
  Member "perceptual quality preserving black box attack against deep learning image classifiers":
    also connected through other hyperedges:
      (none)
  Member "sparse and imperceivable adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "who s afraid of adversarial queries the impact of image modifications on content based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "smooth adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attacking optical character recognition ocr systems with adversarial watermarks")
  Member "a general framework for adversarial examples with objectives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "advjnd generating adversarial examples with just noticeable difference")
  Member "evading defenses to transferable adversarial examples by translation invariant attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "big but imperceptible adversarial perturbations via semantic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing")
  Member "towards imperceptible and robust adversarial example attacks against neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations")

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)