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: "improving transferability of adversarial examples with input diversity"
  Abstract: Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding human-imperceptible perturbations to clean images. However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black-box setting, where the attackers have no knowledge of the model structure and parameters. To this end, we propose to improve the transferability of adversarial examples by creating diverse input patterns. Instead of only using the original images to generate adversarial examples, our method applies random transformations to the input images at each iteration. Extensive experiments on ImageNet show that the proposed attack method can generate adversarial examples that transfer much better to different networks than existing baselines. By evaluating our method against top defense solutions and official baselines from NIPS 2017 adversarial competition, the enhanced attack reaches an average success rate of 73.0%, which outperforms the top-1 attack submission in the NIPS competition by a large margin of 6.6%. We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "image super resolution as a defense against adversarial attacks"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "video salient object detection via fully convolutional networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"

Connected through hyperedge HE-2 (induced by source paper "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"):
  with members:
    - "adversarial attacks and defences competition"
    - "group normalization"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "universal adversarial training"

Connected through hyperedge HE-3 (induced by source paper "evading defenses to transferable adversarial examples by translation invariant attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "quantifying translation invariance in convolutional neural networks"
    - "max mahalanobis linear discriminant analysis networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "towards robust detection of adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "curls whey boosting black box adversarial attacks"):
  with members:
    - "adversarial vision challenge"
    - "learning transferable architectures for scalable image recognition"
    - "magnet a two pronged defense against adversarial examples"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "squeeze and excitation networks"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-5 (induced by source paper "a robust approach for securing audio classification against adversarial attacks"):
  with members:
    - "bottom up broadcast neural network for music genre classification"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "audio adversarial examples targeted attacks on speech to text"
    - "inaudible voice commands"
    - "dropout inference in bayesian neural networks with alpha divergences"

Connected through hyperedge HE-6 (induced by source paper "enhancing cross task transferability of adversarial examples with dispersion reduction"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks and defences competition"
    - "black box adversarial attacks with limited queries and information"
    - "simple black box adversarial perturbations for deep networks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning transferable adversarial examples via ghost networks"):
  with members:
    - "node injection attacks on graphs via reinforcement learning"
    - "adversarial metric attack for person re identification"
    - "adversarial attacks and defences competition"
    - "generating adversarial examples with adversarial networks"
    - "low frequency adversarial perturbation"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "snapshot ensembles train 1 get m for free"
    - "convolutional networks with adaptive inference graphs"

Connected through hyperedge HE-8 (induced by source paper "distorting neural representations to generate highly transferable adversarial examples"):
  with members:
    - "do better imagenet models transfer better"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "synthesizing robust adversarial examples"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual 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.CV(Computer Vision and Pattern Recognition)