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: "ape gan adversarial perturbation elimination with gan"
  Abstract: Although neural networks could achieve state-of-the-art performance while recongnizing images, they often suffer a tremendous defeat from adversarial examples--inputs generated by utilizing imperceptible but intentional perturbation to clean samples from the datasets. How to defense against adversarial examples is an important problem which is well worth researching. So far, very few methods have provided a significant defense to adversarial examples. In this paper, a novel idea is proposed and an effective framework based Generative Adversarial Nets named APE-GAN is implemented to defense against the adversarial examples. The experimental results on three benchmark datasets including MNIST, CIFAR10 and ImageNet indicate that APE-GAN is effective to resist adversarial examples generated from five attacks.

Connected through hyperedge HE-1 (induced by source paper "minimax defense against gradient based adversarial attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial robustness toolbox v1 0 0"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "using an ensemble color space model to tackle adversarial examples"):
  with members:
    - "colornet investigating the importance of color spaces for image classification"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "divide denoise and defend against adversarial attacks"
    - "deflecting adversarial attacks with pixel deflection"
    - "certified defenses against adversarial examples"
    - "the effect of color space selection on detectability and discriminability of colored objects"
    - "magnet a two pronged defense against adversarial examples"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-3 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "improving the generalization of adversarial training with domain adaptation"
    - "exploring the space of black box attacks on deep neural networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "improved training of wasserstein gans"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-4 (induced by source paper "jumprelu a retrofit defense strategy for adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "trust region based adversarial attack on neural networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "purifying adversarial perturbation with adversarially trained auto encoders"):
  with members:
    - "puvae a variational autoencoder to purify adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "defense vae a fast and accurate defense against adversarial attacks"

Connected through hyperedge HE-7 (induced by source paper "variational inference with latent space quantization for adversarial resilience"):
  with members:
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "stochastic activation pruning for robust adversarial defense"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "adversarially robust generalization requires more data"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "adversarial symmetric gans bridging adversarial samples and adversarial networks"):
  with members:
    - "bridging adversarial robustness and gradient interpretability"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "generating adversarial examples with adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "wasserstein divergence for gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

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)