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: "feature distillation dnn oriented jpeg compression against adversarial examples"
  Abstract: Deep Neural Networks (DNNs) have achieved remarkable performance in a myriad of realistic applications. However, recent studies show that well-trained DNNs can be easily misled by adversarial examples (AE) -- the maliciously crafted inputs by introducing small and imperceptible input perturbations. Existing mitigation solutions, such as adversarial training and defensive distillation, suffer from expensive retraining cost and demonstrate marginal robustness improvement against the state-of-the-art attacks like CW family adversarial examples. In this work, we propose a novel low-cost "feature distillation" strategy to purify the adversarial input perturbations of AEs by redesigning the popular image compression framework "JPEG". The proposed "feature distillation" wisely maximizes the malicious feature loss of AE perturbations during image compression while suppressing the distortions of benign features essential for high accurate DNN classification. Experimental results show that our method can drastically reduce the success rate of various state-of-the-art AE attacks by ~60% on average for both CIFAR-10 and ImageNet benchmarks without harming the testing accuracy, outperforming existing solutions like default JPEG compression and "feature squeezing".

Connected through hyperedge HE-1 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-2 (induced by source paper "learning to structure an image with few colors"):
  with members:
    - "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"
    - "clustering with deep learning taxonomy and new methods"
    - "generative adversarial networks for extreme learned image compression"
    - "fast object detection in compressed video"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "hybrid scene compression for visual localization"
    - "compressed video action recognition"

Connected through hyperedge HE-3 (induced by source paper "robustness from simple classifiers"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "max margin adversarial mma training direct input space margin maximization through adversarial training"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards robust detection of adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "one man s trash is another man s treasure resisting adversarial examples by adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "metric learning for adversarial robustness"
    - "hopskipjumpattack a query efficient decision based attack"
    - "defending against adversarial attacks by randomized diversification"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples are a natural consequence of test error in noise"):
  with members:
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial vulnerability for any classifier"
    - "autoaugment learning augmentation policies from data"
    - "the elephant in the room"

Connected through hyperedge HE-7 (induced by source paper "a fourier perspective on model robustness in computer vision"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "band limited training and inference for convolutional neural networks"
    - "simple black box adversarial attacks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"
    - "autoaugment learning augmentation policies from data"
    - "generalisation in humans and deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "colorfool semantic adversarial colorization"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "semantic adversarial examples"
    - "sparsefool a few pixels make a big difference"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

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)