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: "generative adversarial perturbations"
  Abstract: In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models. We present trainable deep neural networks for transforming images to adversarial perturbations. Our proposed models can produce image-agnostic and image-dependent perturbations for both targeted and non-targeted attacks. We also demonstrate that similar architectures can achieve impressive results in fooling classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task. Using extensive experiments on challenging high-resolution datasets such as ImageNet and Cityscapes, we show that our perturbations achieve high fooling rates with small perturbation norms. Moreover, our attacks are considerably faster than current iterative methods at inference time.

Connected through hyperedge HE-1 (induced by source paper "adversarial distributional training for robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "a direct approach to robust deep learning using adversarial networks"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"

Connected through hyperedge HE-2 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "rank consistent ordinal regression for neural networks"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "right for the right reason training agnostic networks"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"

Connected through hyperedge HE-3 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "data augmentation with symbolic to real image translation gans for traffic sign recognition"
    - "spatially transformed adversarial examples"
    - "real to virtual domain unification for end to end autonomous driving"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "multimodal unsupervised image to image translation"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-4 (induced by source paper "frequency tuned universal adversarial attacks"):
  with members:
    - "a survey of black box adversarial attacks on computer vision models"
    - "on the effectiveness of low frequency perturbations"
    - "a geometry inspired decision based attack"
    - "defending against universal attacks through selective feature regeneration"
    - "simple black box adversarial attacks"
    - "a study of black box adversarial attacks in computer vision"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "high frequency component helps explain the generalization of convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "toward adversarial robustness via semi supervised robust training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "theoretically principled trade off between robustness and accuracy"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-6 (induced by source paper "ai gan attack inspired generation of adversarial examples"):
  with members:
    - "advgan harnessing latent layers for adversary generation"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "generative adversarial text to image synthesis"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "fine grained synthesis of unrestricted adversarial examples"):
  with members:
    - "neural puppet generative layered cartoon characters"
    - "certified adversarial robustness via randomized smoothing"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "spatially transformed adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-8 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "deepcorrect correcting dnn models against image distortions"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"

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)