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: "generative adversarial trainer defense to adversarial perturbations with gan"
  Abstract: We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning.

Connected through hyperedge HE-1 (induced by source paper "distortion agnostic deep watermarking"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "romark a robust watermarking system using adversarial training"
    - "steganogan high capacity image steganography with gans"
    - "robust invisible video watermarking with attention"
    - "adversarial training for free"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "redmark framework for residual diffusion watermarking on deep networks"
    - "neural joint source channel coding"

Connected through hyperedge HE-2 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-3 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial attacks and defences a survey"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-5 (induced by source paper "a kernelized manifold mapping to diminish the effect of adversarial perturbations"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "vulnerability analysis of chest x ray image classification against adversarial attacks"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "blind pre processing a robust defense method against adversarial examples"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"

Connected through hyperedge HE-6 (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-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 "defense methods against adversarial examples for recurrent neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "robust neural malware detection models for emulation sequence learning"

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.LG(Machine Learning)