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 "at gan a generative attack model for adversarial transferring on generative adversarial nets"):
  Abstract: We propose a new attack model called AT-GAN that transfers generative adversarial nets (GANs) for adversarial attacks. Different from existing attacks that add small perturbations to the input images, AT-GAN tries to explore the distribution of adversarial instances so as to directly generate the adversarial examples from any random noise. In this way, the generated adversarial examples are not limited to any natual images. Also, compared with the ioneer work using GAN that do iterations of gradient descent to search for a good noise in the neighborhood of an original random noise such that the corresponding output of GAN is an adversial example, our model is a generative model that tries to learn the distribution of the adversial examples. Once AT-GAN is trained, it can generate adversarial images and the output is not limited to the input noise. Experiments show that AT-GAN is very fast and can generate plenty of adversarial instances that look more realistic to human eyes, AT-GAN yields a higher attack success rate under various adversarial training defenses for semi-whitebox as well as black-box attack settings, and AT-GAN can learn a distribution of adversarial examples that is very close to the distribution of the real data.

Includes members:
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "improving the generalization of adversarial training with domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "exploring the space of black box attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples")
  Member "generating adversarial examples with adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "constructing unrestricted adversarial examples with generative models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  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 "defense against adversarial attacks using high level representation guided denoiser":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "ape gan adversarial perturbation elimination with gan":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimax defense against gradient based adversarial attacks")

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)