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: "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
  Abstract: Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data. Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems. To be immune to such attacks, it is desirable to have a pre-processing mechanism which removes these effects causing misclassification while keeping the content of the image. JPEG and JPEG2000 are well-known image compression techniques which suppress the high-frequency content taking the human visual system into account. JPEG has been also shown to be an effective method for reducing adversarial noise. In this paper, we propose applying JPEG2000 compression as an alternative and systematically compare the classification performance of adversarial images compressed using JPEG and JPEG2000 at different target PSNR values and maximum compression levels. Our experiments show that JPEG2000 is more effective in reducing adversarial noise as it allows higher compression rates with less distortion and it does not introduce blocking artifacts.

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"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "attack type agnostic perceptual enhancement of adversarial images"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "learning deep features for discriminative localization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "feature distillation dnn oriented jpeg compression against adversarial examples"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-6 (induced by source paper "noise flooding for detecting audio adversarial examples against automatic speech recognition"):
  with members:
    - "did you hear that adversarial examples against automatic speech recognition"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deflecting adversarial attacks with pixel deflection"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "assessing threat of adversarial examples on deep neural networks"
    - "xgboost a scalable tree boosting system"

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)