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: "comdefend an efficient image compression model to defend adversarial examples"
  Abstract: Deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples. Specifically, adding imperceptible perturbations to clean images can fool the well trained deep neural networks. In this paper, we propose an end-to-end image compression model to defend adversarial examples: \textbf{ComDefend}. The proposed model consists of a compression convolutional neural network (ComCNN) and a reconstruction convolutional neural network (ResCNN). The ComCNN is used to maintain the structure information of the original image and purify adversarial perturbations. And the ResCNN is used to reconstruct the original image with high quality. In other words, ComDefend can transform the adversarial image to its clean version, which is then fed to the trained classifier. Our method is a pre-processing module, and does not modify the classifier's structure during the whole process. Therefore, it can be combined with other model-specific defense models to jointly improve the classifier's robustness. A series of experiments conducted on MNIST, CIFAR10 and ImageNet show that the proposed method outperforms the state-of-the-art defense methods, and is consistently effective to protect classifiers against adversarial attacks.

Connected through hyperedge HE-1 (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"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hybrid scene compression for visual localization"
    - "compressed video action recognition"

Connected through hyperedge HE-2 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "adversarially robust generalization just requires more unlabeled data"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "robust local features for improving the generalization of adversarial training"
    - "adversarial attacks and defences competition"
    - "improving the generalization of adversarial training with domain adaptation"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-3 (induced by source paper "applying tensor decomposition to image for robustness against adversarial attack"):
  with members:
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-4 (induced by source paper "dynamic divide and conquer adversarial training for robust semantic segmentation"):
  with members:
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "improving the generalization of adversarial training with domain adaptation"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-5 (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"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "deep defense training dnns with improved adversarial robustness"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-6 (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-7 (induced by source paper "random directional attack for fooling deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "semantic adversarial examples"
    - "disentangling adversarial robustness and generalization"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "divide denoise and defend against adversarial attacks"

Connected through hyperedge HE-8 (induced by source paper "had gan a human perception auxiliary defense gan to defend adversarial examples"):
  with members:
    - "robust neural networks using randomized adversarial training"
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "generating adversarial examples with conditional generative adversarial net"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "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"

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)