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: "shield fast practical defense and vaccination for deep learning using jpeg compression"
  Abstract: The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This underscores the urgent need for practical defense that can be readily deployed to combat attacks in real-time. Observing that many attack strategies aim to perturb image pixels in ways that are visually imperceptible, we place JPEG compression at the core of our proposed Shield defense framework, utilizing its capability to effectively "compress away" such pixel manipulation. To immunize a DNN model from artifacts introduced by compression, Shield "vaccinates" a model by re-training it with compressed images, where different compression levels are applied to generate multiple vaccinated models that are ultimately used together in an ensemble defense. On top of that, Shield adds an additional layer of protection by employing randomization at test time that compresses different regions of an image using random compression levels, making it harder for an adversary to estimate the transformation performed. This novel combination of vaccination, ensembling, and randomization makes Shield a fortified multi-pronged protection. We conducted extensive, large-scale experiments using the ImageNet dataset, and show that our approaches eliminate up to 94% of black-box attacks and 98% of gray-box attacks delivered by the recent, strongest attacks, such as Carlini-Wagner's L2 and DeepFool. Our approaches are fast and work without requiring knowledge about the model.

Connected through hyperedge HE-1 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples"):
  with members:
    - "protecting jpeg images against adversarial attacks"
    - "adversarial robustness toolbox v1 0 0"
    - "deflecting adversarial attacks with pixel deflection"
    - "exploiting the inherent limitation of l0 adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-3 (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"
    - "lavan localized and visible adversarial noise"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-4 (induced by source paper "sad saliency based defenses against adversarial examples"):
  with members:
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "magnet a two pronged defense against adversarial examples"
    - "synthesizing robust adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "tensorshield tensor based defense against adversarial attacks on images"):
  with members:
    - "the efficacy of shield under different threat models"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "tensorly tensor learning in python"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "lower bounds on adversarial robustness from optimal transport"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "analyzing federated learning through an adversarial lens"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-7 (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"
    - "improving transferability of adversarial examples with input diversity"

Connected through hyperedge HE-8 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "deepcorrect correcting dnn models against image distortions"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"

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)