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 "colorfool semantic adversarial colorization"):
  Abstract: Adversarial attacks that generate small L_p-norm perturbations to mislead classifiers have limited success in black-box settings and with unseen classifiers. These attacks are also fragile with defenses that use denoising filters and to adversarial training procedures. Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers. However, unrestricted perturbations may be noticeable to humans. In this paper, we propose a content-based black-box adversarial attack that generates unrestricted perturbations by exploiting image semantics to selectively modify colors within chosen ranges that are perceived as natural by humans. We show that the proposed approach, ColorFool, outperforms in terms of success rate, robustness to defense frameworks and transferability five state-of-the-art adversarial attacks on two different tasks, scene and object classification, when attacking three state-of-the-art deep neural networks using three standard datasets. We will make the code of the proposed approach and the whole evaluation framework publicly available.

Includes members:
  Member "adversarial defense by restricting the hidden space of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of black box adversarial attacks on computer vision models")
  Member "evading defenses to transferable adversarial examples by translation invariant attacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing")
  Member "semantic adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")
  Member "feature distillation dnn oriented jpeg compression against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an empirical evaluation of perturbation based defenses")
  Member "sparsefool a few pixels make a big difference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare 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.CV(Computer Vision and Pattern Recognition)