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: "lavan localized and visible adversarial noise"
  Abstract: Most works on adversarial examples for deep-learning based image classifiers use noise that, while small, covers the entire image. We explore the case where the noise is allowed to be visible but confined to a small, localized patch of the image, without covering any of the main object(s) in the image. We show that it is possible to generate localized adversarial noises that cover only 2% of the pixels in the image, none of them over the main object, and that are transferable across images and locations, and successfully fool a state-of-the-art Inception v3 model with very high success rates.

Connected through hyperedge HE-1 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "anomaly detection using one class neural networks"
    - "review of deep learning"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "visionguard runtime detection of adversarial inputs to perception systems"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "attacking optical character recognition ocr systems with adversarial watermarks"):
  with members:
    - "smooth adversarial examples"
    - "harmonic adversarial attack method"
    - "adversarial patch"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "on the statistical detection of 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-4 (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-5 (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"
    - "local gradients smoothing defense against localized adversarial attacks"

Connected through hyperedge HE-6 (induced by source paper "detecting patch adversarial attacks with image residuals"):
  with members:
    - "de randomized smoothing for certifiable defense against patch attacks"
    - "certified defenses for adversarial patches"
    - "one pixel attack for fooling deep neural networks"
    - "a fourier perspective on model robustness in computer vision"
    - "adversarial patch"
    - "sentinet detecting physical attacks against deep learning systems"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "physgan generating physical world resilient adversarial examples for autonomous driving"):
  with members:
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "spatially transformed adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-8 (induced by source paper "scratch that an evolution based adversarial attack against neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "they might not be giants crafting black box adversarial examples with fewer queries using particle swarm optimization"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial patch"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "genattack practical black box attacks with gradient free optimization"

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)