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: "local gradients smoothing defense against localized adversarial attacks"
  Abstract: Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently introduced localized attacks, Localized and Visible Adversarial Noise (LaVAN) and Adversarial patch, pose a new challenge to deep learning security by adding adversarial noise only within a specific region without affecting the salient objects in an image. Driven by the observation that such attacks introduce concentrated high-frequency changes at a particular image location, we have developed an effective method to estimate noise location in gradient domain and transform those high activation regions caused by adversarial noise in image domain while having minimal effect on the salient object that is important for correct classification. Our proposed Local Gradients Smoothing (LGS) scheme achieves this by regularizing gradients in the estimated noisy region before feeding the image to DNN for inference. We have shown the effectiveness of our method in comparison to other defense methods including Digital Watermarking, JPEG compression, Total Variance Minimization (TVM) and Feature squeezing on ImageNet dataset. In addition, we systematically study the robustness of the proposed defense mechanism against Back Pass Differentiable Approximation (BPDA), a state of the art attack recently developed to break defenses that transform an input sample to minimize the adversarial effect. Compared to other defense mechanisms, LGS is by far the most resistant to BPDA in localized adversarial attack setting.

Connected through hyperedge HE-1 (induced by source paper "de randomized smoothing for certifiable defense against patch attacks"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "randomized smoothing of all shapes and sizes"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "certified defenses for adversarial patches"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"

Connected through hyperedge HE-2 (induced by source paper "certified defenses for adversarial patches"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "enhancing gradient based attacks with symbolic intervals"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial generative nets neural network attacks on state of the art face recognition"

Connected through hyperedge HE-3 (induced by source paper "dopa a comprehensive cnn detection methodology against physical adversarial attacks"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a multiversion programming inspired approach to detecting audio adversarial examples"
    - "characterizing audio adversarial examples using temporal dependency"
    - "robust physical world attacks on deep learning models"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "lance a comprehensive and lightweight cnn defense methodology against physical adversarial attacks on embedded multimedia applications"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a multiversion programming inspired approach to detecting audio adversarial examples"
    - "characterizing audio adversarial examples using temporal dependency"
    - "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)