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: "image super resolution as a defense against adversarial attacks"
  Abstract: Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical security-sensitive systems. This paper proposes a computationally efficient image enhancement approach that provides a strong defense mechanism to effectively mitigate the effect of such adversarial perturbations. We show that deep image restoration networks learn mapping functions that can bring off-the-manifold adversarial samples onto the natural image manifold, thus restoring classification towards correct classes. A distinguishing feature of our approach is that, in addition to providing robustness against attacks, it simultaneously enhances image quality and retains models performance on clean images. Furthermore, the proposed method does not modify the classifier or requires a separate mechanism to detect adversarial images. The effectiveness of the scheme has been demonstrated through extensive experiments, where it has proven a strong defense in gray-box settings. The proposed scheme is simple and has the following advantages: (1) it does not require any model training or parameter optimization, (2) it complements other existing defense mechanisms, (3) it is agnostic to the attacked model and attack type and (4) it provides superior performance across all popular attack algorithms. Our codes are publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"

Connected through hyperedge HE-2 (induced by source paper "double backpropagation for training autoencoders against adversarial attack"):
  with members:
    - "when does label smoothing help"
    - "adversarial attacks on variational autoencoders"
    - "sparse dnns with improved adversarial robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "deep defense training dnns with improved adversarial robustness"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "adversarial defense by restricting the hidden space of deep neural networks"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial attacks and defences competition"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial logit pairing"
    - "improving the generalization of adversarial training with domain adaptation"
    - "interpreting adversarial robustness a view from decision surface in input space"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"

Connected through hyperedge HE-4 (induced by source paper "adversarially robust distillation"):
  with members:
    - "defensive quantization when efficiency meets robustness"
    - "adversarial training for free"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "feature denoising for improving adversarial robustness"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"
    - "adversarial logit pairing"
    - "autoaugment learning augmentation policies from data"
    - "knowledge distillation with feature maps for image classification"

Connected through hyperedge HE-5 (induced by source paper "an adaptive view of adversarial robustness from test time smoothing defense"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "feature denoising for improving adversarial robustness"
    - "safety and trustworthiness of deep neural networks a survey"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "robotic grasp detection using deep convolutional neural networks"
    - "end to end learning for self driving cars"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-6 (induced by source paper "evaluating robustness of deep image super resolution against adversarial attacks"):
  with members:
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "deep learning based image super resolution considering quantitative and perceptual quality"
    - "enhanced deep residual networks for single image super resolution"
    - "mitigating adversarial effects through randomization"

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)