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: "defense against adversarial attacks with saak transform"
  Abstract: Deep neural networks (DNNs) are known to be vulnerable to adversarial perturbations, which imposes a serious threat to DNN-based decision systems. In this paper, we propose to apply the lossy Saak transform to adversarially perturbed images as a preprocessing tool to defend against adversarial attacks. Saak transform is a recently-proposed state-of-the-art for computing the spatial-spectral representations of input images. Empirically, we observe that outputs of the Saak transform are very discriminative in differentiating adversarial examples from clean ones. Therefore, we propose a Saak transform based preprocessing method with three steps: 1) transforming an input image to a joint spatial-spectral representation via the forward Saak transform, 2) apply filtering to its high-frequency components, and, 3) reconstructing the image via the inverse Saak transform. The processed image is found to be robust against adversarial perturbations. We conduct extensive experiments to investigate various settings of the Saak transform and filtering functions. Without harming the decision performance on clean images, our method outperforms state-of-the-art adversarial defense methods by a substantial margin on both the CIFAR-10 and ImageNet datasets. Importantly, our results suggest that adversarial perturbations can be effectively and efficiently defended using state-of-the-art frequency analysis.

Connected through hyperedge HE-1 (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-2 (induced by source paper "diminishing the effect of adversarial perturbations via refining feature representation"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "on evaluating adversarial robustness"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "vulnerability analysis of chest x ray image classification against adversarial attacks"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "robustness of saak transform against adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "feature denoising for improving adversarial robustness"
    - "universal adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "on data driven saak transform"

Connected through hyperedge HE-4 (induced by source paper "visualization discriminability and applications of interpretable saak features"):
  with members:
    - "on data driven saak transform"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "on the statistical detection of adversarial examples"
    - "adversarial examples attacks and defenses for deep learning"
    - "understanding convolutional neural networks with a mathematical model"
    - "adversarial examples in the physical world"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"):
  with members:
    - "the singular values of convolutional layers"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "countering adversarial images using input transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)