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 "targeted nonlinear adversarial perturbations in images and videos"):
  Abstract: We introduce a method for learning adversarial perturbations targeted to individual images or videos. The learned perturbations are found to be sparse while at the same time containing a high level of feature detail. Thus, the extracted perturbations allow a form of object or action recognition and provide insights into what features the studied deep neural network models consider important when reaching their classification decisions. From an adversarial point of view, the sparse perturbations successfully confused the models into misclassifying, although the perturbed samples still belonged to the same original class by visual examination. This is discussed in terms of a prospective data augmentation scheme. The sparse yet high-quality perturbations may also be leveraged for image or video compression.

Includes members:
  Member "sparse adversarial perturbations for videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")
  Member "adversarial transformation networks learning to generate adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "the kinetics human action video dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "interpretable explanations of black boxes by meaningful perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "end to end learning for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "delving into transferable adversarial examples and black box attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "towards evaluating the robustness of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "deep image scaling up image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stochastic optimization of plain convolutional neural networks with simple methods")

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)