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: "attacking convolutional neural network using differential evolution"
  Abstract: The output of Convolutional Neural Networks (CNN) has been shown to be discontinuous which can make the CNN image classifier vulnerable to small well-tuned artificial perturbations. That is, images modified by adding such perturbations(i.e. adversarial perturbations) that make little difference to human eyes, can completely alter the CNN classification results. In this paper, we propose a practical attack using differential evolution(DE) for generating effective adversarial perturbations. We comprehensively evaluate the effectiveness of different types of DEs for conducting the attack on different network structures. The proposed method is a black-box attack which only requires the miracle feedback of the target CNN systems. The results show that under strict constraints which simultaneously control the number of pixels changed and overall perturbation strength, attacking can achieve 72.29%, 78.24% and 61.28% non-targeted attack success rates, with 88.68%, 99.85% and 73.07% confidence on average, on three common types of CNNs. The attack only requires modifying 5 pixels with 20.44, 14.76 and 22.98 pixel values distortion. Thus, the result shows that the current DNNs are also vulnerable to such simpler black-box attacks even under very limited attack conditions.

Connected through hyperedge HE-1 (induced by source paper "adversarial sample detection for deep neural network through model mutation testing"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "spatially transformed adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "towards robust training of neural networks by regularizing adversarial gradients"

Connected through hyperedge HE-2 (induced by source paper "structure matters towards generating transferable adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "adversarial training for free"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "semantic adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-3 (induced by source paper "perceptual quality preserving black box attack against deep learning image classifiers"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "exploring the space of black box attacks on deep neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "sparsefool a few pixels make a big difference"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "spatially transformed adversarial examples"
    - "audio adversarial examples targeted attacks on speech to text"
    - "with friends like these who needs adversaries"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "geometric robustness of deep networks analysis and improvement"
    - "facial attributes accuracy and adversarial robustness"

Connected through hyperedge HE-5 (induced by source paper "detecting adversarial samples for deep neural networks through mutation testing"):
  with members:
    - "spatially transformed adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards proving the adversarial robustness of deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "extending defensive distillation"
    - "feature guided black box safety testing of deep neural networks"
    - "fast feature fool a data independent approach to universal adversarial perturbations"

Connected through hyperedge HE-6 (induced by source paper "structure preserving transformation generating diverse and transferable adversarial examples"):
  with members:
    - "semantic adversarial examples"
    - "adversarial patch"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "distributionally adversarial attack"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"

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)