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: "robust convolutional neural networks under adversarial noise"
  Abstract: Recent studies have shown that Convolutional Neural Networks (CNNs) are vulnerable to a small perturbation of input called "adversarial examples". In this work, we propose a new feedforward CNN that improves robustness in the presence of adversarial noise. Our model uses stochastic additive noise added to the input image and to the CNN models. The proposed model operates in conjunction with a CNN trained with either standard or adversarial objective function. In particular, convolution, max-pooling, and ReLU layers are modified to benefit from the noise model. Our feedforward model is parameterized by only a mean and variance per pixel which simplifies computations and makes our method scalable to a deep architecture. From CIFAR-10 and ImageNet test, the proposed model outperforms other methods and the improvement is more evident for difficult classification tasks or stronger adversarial noise.

Connected through hyperedge HE-1 (induced by source paper "adversarial example detection and classification with asymmetrical adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "conditional generative models are not robust"
    - "natural and adversarial error detection using invariance to image transformations"
    - "consistency regularization for generative adversarial networks"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "towards the first adversarially robust neural network model on mnist"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "divide and conquer adversarial detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "natural and adversarial error detection using invariance to image transformations"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "generating adversarial examples with adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "style transfer based image synthesis as an efficient regularization technique in deep learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "text to image synthesis using generative adversarial networks"
    - "staingan stain style transfer for digital histological images"
    - "gans for medical image analysis"
    - "random erasing data augmentation"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "an empirical investigation of randomized defenses against adversarial attacks"):
  with members:
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "towards robust neural networks via random self ensemble"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (induced by source paper "a general framework for uncertainty estimation in deep learning"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "where is the information in a deep neural network"
    - "lightweight probabilistic deep networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "the limits and potentials of deep learning for robotics"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"

Connected through hyperedge HE-6 (induced by source paper "regularized deep networks in intelligent transportation systems a taxonomy and a case study"):
  with members:
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "bridgeout stochastic bridge regularization for deep neural networks"
    - "exploring the landscape of spatial robustness"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "fractalnet ultra deep neural networks without residuals"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "an overview of gradient descent optimization algorithms"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "detecting adversarial perturbations with saliency"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"
    - "on the statistical detection of adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "early methods for detecting adversarial images"
    - "measuring neural net robustness with constraints"
    - "adversarial examples detection in deep networks with convolutional filter statistics"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-8 (induced by source paper "an improved evaluation framework for generative adversarial networks"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "stacked generative adversarial networks"

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)