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: "deep defense training dnns with improved adversarial robustness"
  Abstract: Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named "deep defense". Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "double backpropagation for training autoencoders against adversarial attack"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "when does label smoothing help"
    - "adversarial attacks on variational autoencoders"
    - "sparse dnns with improved adversarial robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "a direct approach to robust deep learning using adversarial networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-4 (induced by source paper "defense against adversarial attacks using feature scattering based adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "joint adversarial training incorporating both spatial and pixel attacks"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"

Connected through hyperedge HE-5 (induced by source paper "metric learning for adversarial robustness"):
  with members:
    - "hardness aware deep metric learning"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "an analysis of the t sne algorithm for data visualization"
    - "formal security analysis of neural networks using symbolic intervals"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-6 (induced by source paper "adversarial learning with margin based triplet embedding regularization"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "deep facial expression recognition a survey"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-7 (induced by source paper "improved detection of adversarial attacks via penetration distortion maximization"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "stochastic activation pruning for robust adversarial defense"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "max margin adversarial mma training direct input space margin maximization through adversarial training"

Connected through hyperedge HE-8 (induced by source paper "towards robust locally linear deep networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "scaling provable adversarial defenses"
    - "provable robustness of relu networks via maximization of linear regions"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on the robustness of interpretability methods"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "smoothgrad removing noise by adding noise"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

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)