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: "playing the game of universal adversarial perturbations"
  Abstract: We study the problem of learning classifiers robust to universal adversarial perturbations. While prior work approaches this problem via robust optimization, adversarial training, or input transformation, we instead phrase it as a two-player zero-sum game. In this new formulation, both players simultaneously play the same game, where one player chooses a classifier that minimizes a classification loss whilst the other player creates an adversarial perturbation that increases the same loss when applied to every sample in the training set. By observing that performing a classification (respectively creating adversarial samples) is the best response to the other player, we propose a novel extension of a game-theoretic algorithm, namely fictitious play, to the domain of training robust classifiers. Finally, we empirically show the robustness and versatility of our approach in two defence scenarios where universal attacks are performed on several image classification datasets -- CIFAR10, CIFAR100 and ImageNet.

Connected through hyperedge HE-1 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"

Connected through hyperedge HE-2 (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"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"
    - "divide denoise and defend against adversarial attacks"

Connected through hyperedge HE-3 (induced by source paper "confidence calibrated adversarial training generalizing to unseen attacks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "square attack a query efficient black box adversarial attack via random search"
    - "adversarial robustness against the union of multiple perturbation models"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-4 (induced by source paper "the attack generator a systematic approach towards constructing adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "spatially transformed adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "defending against universal perturbations with shared adversarial training"):
  with members:
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "universal adversarial training"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "with friends like these who needs adversaries"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "nag network for adversary generation"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial training"):
  with members:
    - "adversarial attacks on copyright detection systems"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "adversarial patch"
    - "ask acquire and attack data free uap generation using class impressions"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "defending against universal perturbations with shared adversarial training"
    - "generating adversarial examples with adversarial networks"
    - "generalizable data free objective for crafting universal adversarial perturbations"

Connected through hyperedge HE-7 (induced by source paper "universal decision based black box perturbations breaking security through obscurity defenses"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "adversarial patch"
    - "exploring the space of black box attacks on deep neural networks"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "generative adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

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)