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: "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.

Connected through hyperedge HE-1 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "robust local features for improving the generalization of adversarial training"
    - "adversarial attacks and defences competition"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "improving the generalization of adversarial training with domain adaptation"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-2 (induced by source paper "advspade realistic unrestricted attacks for semantic segmentation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "adaptive generation of unrestricted adversarial inputs"
    - "3d aware scene manipulation via inverse graphics"
    - "unified perceptual parsing for scene understanding"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "generating adversarial examples with adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "spoc spoofing camera fingerprints"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "faceforensics learning to detect manipulated facial images"
    - "source generator attribution via inversion"
    - "advfaces adversarial face synthesis"
    - "exposing gan synthesized faces using landmark locations"
    - "detecting and simulating artifacts in gan fake images"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "make a face towards arbitrary high fidelity face manipulation"

Connected through hyperedge HE-4 (induced by source paper "advfaces adversarial face synthesis"):
  with members:
    - "a general framework for adversarial examples with objectives"
    - "spatially transformed adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "fast geometrically perturbed adversarial faces"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-5 (induced by source paper "adaptive generation of unrestricted adversarial inputs"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "global adversarial attacks for assessing deep learning robustness"
    - "how good is my gan"
    - "rmdl random multimodel deep learning for classification"
    - "towards the first adversarially robust neural network model on mnist"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-6 (induced by source paper "robust local features for improving the generalization of adversarial training"):
  with members:
    - "nesterov accelerated gradient and scale invariance for adversarial attacks"
    - "a direct approach to robust deep learning using adversarial networks"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "interpreting adversarially trained convolutional neural networks"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "improving the generalization of adversarial training with domain adaptation"

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)