human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "adversarial attacks on variational autoencoders"
  Abstract: Adversarial attacks are malicious inputs that derail machine-learning models. We propose a scheme to attack autoencoders, as well as a quantitative evaluation framework that correlates well with the qualitative assessment of the attacks. We assess --- with statistically validated experiments --- the resistance to attacks of three variational autoencoders (simple, convolutional, and DRAW) in three datasets (MNIST, SVHN, CelebA), showing that both DRAW's recurrence and attention mechanism lead to better resistance. As autoencoders are proposed for compressing data --- a scenario in which their safety is paramount --- we expect more attention will be given to adversarial attacks on them.

Connected through hyperedge HE-1 (induced by source paper "double backpropagation for training autoencoders against adversarial attack"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "when does label smoothing help"
    - "sparse dnns with improved adversarial robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "deep defense training dnns with improved adversarial robustness"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "type i attack for generative models"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "semantic adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "adversarial attack type i cheat classifiers by significant changes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Question: Please tell me which class the center node 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)