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: "sparse adversarial perturbations for videos"
  Abstract: Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored. Compared with images, attacking a video needs to consider not only spatial cues but also temporal cues. Moreover, to improve the imperceptibility as well as reduce the computation cost, perturbations should be added on as fewer frames as possible, i.e., adversarial perturbations are temporally sparse. This further motivates the propagation of perturbations, which denotes that perturbations added on the current frame can transfer to the next frames via their temporal interactions. Thus, no (or few) extra perturbations are needed for these frames to misclassify them. To this end, we propose an l2,1-norm based optimization algorithm to compute the sparse adversarial perturbations for videos. We choose the action recognition as the targeted task, and networks with a CNN+RNN architecture as threat models to verify our method. Thanks to the propagation, we can compute perturbations on a shortened version video, and then adapt them to the long version video to fool DNNs. Experimental results on the UCF101 dataset demonstrate that even only one frame in a video is perturbed, the fooling rate can still reach 59.7%.

Connected through hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior"):
  with members:
    - "sparse black box video attack with reinforcement learning"
    - "heuristic black box adversarial attacks on video recognition models"
    - "deep video inpainting"
    - "appending adversarial frames for universal video attack"
    - "sequence level semantics aggregation for video object detection"
    - "black box adversarial attacks on video recognition models"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "patternless adversarial attacks on video recognition networks"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "targeted nonlinear adversarial perturbations in images and videos"
    - "slowfast networks for video recognition"
    - "adversarial perturbations against real time video classification systems"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "a survey on deep learning in medical image analysis"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-3 (induced by source paper "sparse black box video attack with reinforcement learning"):
  with members:
    - "identifying and resisting adversarial videos using temporal consistency"
    - "black box adversarial attacks on video recognition models"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial perturbations against real time video classification systems"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "deep reinforcement learning an overview"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "temporal sparse adversarial attack on gait recognition"):
  with members:
    - "fine grained synthesis of unrestricted adversarial examples"
    - "appending adversarial frames for universal video attack"
    - "a style based generator architecture for generative adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial machine learning at scale"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "smart skeletal motion action recognition attack"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "meshadv adversarial meshes for visual recognition"
    - "adversarial attacks on time series"
    - "adversarial attack on skeleton based human action recognition"
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "spatio temporal manifold learning for human motions via long horizon modeling"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "spatial aware online adversarial perturbations against visual object tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "robust adversarial perturbation on deep proposal based models"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"

Connected through hyperedge HE-8 (induced by source paper "black box adversarial attacks on video recognition models"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "targeted nonlinear adversarial perturbations in images and videos"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling multimodal clues in a hybrid deep learning framework for video classification"

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)