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: "rob gan generator discriminator and adversarial attacker"
  Abstract: We study two important concepts in adversarial deep learning---adversarial training and generative adversarial network (GAN). Adversarial training is the technique used to improve the robustness of discriminator by combining adversarial attacker and discriminator in the training phase. GAN is commonly used for image generation by jointly optimizing discriminator and generator. We show these two concepts are indeed closely related and can be used to strengthen each other---adding a generator to the adversarial training procedure can improve the robustness of discriminators, and adding an adversarial attack to GAN training can improve the convergence speed and lead to better generators. Combining these two insights, we develop a framework called Rob-GAN to jointly optimize generator and discriminator in the presence of adversarial attacks---the generator generates fake images to fool discriminator; the adversarial attacker perturbs real images to fool the discriminator, and the discriminator wants to minimize loss under fake and adversarial images. Through this end-to-end training procedure, we are able to simultaneously improve the convergence speed of GAN training, the quality of synthetic images, and the robustness of discriminator under strong adversarial attacks. Experimental results demonstrate that the obtained classifier is more robust than the state-of-the-art adversarial training approach, and the generator outperforms SN-GAN on ImageNet-143.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space"):
  with members:
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "an empirical investigation of randomized defenses against adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "deep learning for single image super resolution a brief review"
    - "deep learning for multiple image super resolution"
    - "sampling and super resolution of sparse signals beyond the fourier domain"
    - "deep learning for image super resolution a survey"

Connected through hyperedge HE-2 (induced by source paper "ml loo detecting adversarial examples with feature attribution"):
  with members:
    - "on the in fidelity and sensitivity for explanations"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "training verified learners with learned verifiers"
    - "adversarial learning and explainability in structured datasets"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "concise and stable explanations using adversarial training"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "convergence of adversarial training in overparametrized neural networks"):
  with members:
    - "a gram gauss newton method learning overparameterized deep neural networks for regression problems"
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "adversarial training for free"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-4 (induced by source paper "self supervised gan analysis and improvement with multi class minimax game"):
  with members:
    - "an improved self supervised gan via adversarial training"
    - "self supervised gans via auxiliary rotation loss"
    - "improving gan with neighbors embedding and gradient matching"
    - "varying k lipschitz constraint for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "dist gan an improved gan using distance constraints"

Connected through hyperedge HE-5 (induced by source paper "robust decision trees against adversarial examples"):
  with members:
    - "gradient regularized budgeted boosting"
    - "theoretically principled trade off between robustness and accuracy"
    - "multi layered gradient boosting decision trees"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-6 (induced by source paper "perturbations are not enough generating adversarial examples with spatial distortions"):
  with members:
    - "spatially transformed adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "robustness of rotation equivariant networks to adversarial perturbations"
    - "improved network robustness with adversary critic"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "adv bnn improved adversarial defense through robust bayesian neural network"):
  with members:
    - "adversarial logit pairing"
    - "adversarially robust generalization requires more data"
    - "black box adversarial attacks with limited queries and information"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "towards robust neural networks via random self ensemble"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"

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.LG(Machine Learning)