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: "advhat real world adversarial attack on arcface face id system"
  Abstract: In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common color printer and put it on the hat. The adversarial sticker is prepared with a novel algorithm for off-plane transformations of the image which imitates sticker location on the hat. Such an approach confuses the state-of-the-art public Face ID model LResNet100E-IR, ArcFace@ms1m-refine-v2 and is transferable to other Face ID models.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "hopskipjumpattack a query efficient decision based attack"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"

Connected through hyperedge HE-2 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models"):
  with members:
    - "strong baseline defenses against clean label poisoning attacks"
    - "anonymousnet natural face de identification with measurable privacy"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "privacy protective gan for face de identification"

Connected through hyperedge HE-4 (induced by source paper "design and interpretation of universal adversarial patches in face detection"):
  with members:
    - "towards adversarially robust object detection"
    - "theoretically principled trade off between robustness and accuracy"
    - "a general framework for adversarial examples with objectives"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "on physical adversarial patches for object detection"
    - "dsfd dual shot face detector"
    - "adversarial patch"

Connected through hyperedge HE-5 (induced by source paper "making an invisibility cloak real world adversarial attacks on object detectors"):
  with members:
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "fcos fully convolutional one stage object detection"
    - "adversarial patch"
    - "transferable adversarial attacks for image and video object detection"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust physical world attacks on deep learning models"
    - "focal loss for dense object detection"
    - "on the robustness of semantic segmentation models to adversarial attacks"

Connected through hyperedge HE-6 (induced by source paper "learning to characterize adversarial subspaces"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "attention is all you need"
    - "practical black box attacks against machine learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "region wise attack on efficient generation of robust physical adversarial examples"):
  with members:
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "genattack practical black box attacks with gradient free optimization"
    - "robust physical world attacks on deep learning models"
    - "learning transferable architectures for scalable image recognition"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "on adversarial patches real world attack on arcface 100 face recognition system"):
  with members:
    - "adversarial patch"
    - "arcface additive angular margin loss for deep face recognition"
    - "synthesizing robust adversarial examples"
    - "adversarial examples in the physical world"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "spatial transformer networks"
    - "learning face representation from scratch"
    - "intriguing properties of neural networks"

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)