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: "a direct approach to robust deep learning using adversarial networks"
  Abstract: Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network (GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.

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"
    - "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"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "improving robustness of deep learning based image reconstruction"):
  with members:
    - "gan based projector for faster recovery with convergence guarantees in linear inverse problems"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "scaling provable adversarial defenses"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"
    - "image reconstruction by domain transform manifold learning"

Connected through hyperedge HE-3 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "defensive quantization when efficiency meets robustness"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training for faster adversarial robustness verification via inducing relu stability"

Connected through hyperedge HE-4 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"):
  with members:
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-5 (induced by source paper "black box adversarial attack with transferable model based embedding"):
  with members:
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "simple black box adversarial attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adversarial vision challenge"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

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"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "mnist c a robustness benchmark for computer vision"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "on evaluating adversarial robustness"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-8 (induced by source paper "learning to defense by learning to attack"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "on evaluating adversarial robustness"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "generating adversarial examples with adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)