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: "seeing isn t believing practical adversarial attack against object detectors"
  Abstract: In this paper, we design the first practical adversarial attacks against object detectors in realistic situations: the printed adversarial examples (AEs) placed with different angles and distances, could hide or create an object to deceive object detectors. To improve the robustness of AEs, we propose the novel nested AEs and introduce the image transformation techniques to simulate the variance factors such as distances, angles, illuminations, in the physical world. To let the AEs converge with so many constraints, we also design batch-variation momentum training. Evaluation results show that our adversarial examples work in real environments, capable of attacking state-of-the-art real-time object detectors (e.g., YOLO V3 and faster-RCNN) with the success rate up to 92.4% (with the distance ranging from 1m to 25m and wide angles from -60{\deg}to 60{\deg}). Our AEs are also shown to be highly transferable. Four state-of-the-art black-box models could be attacked by our AEs with high success rates.

Connected through hyperedge HE-1 (induced by source paper "spark spatial aware online incremental attack against visual tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "a twofold siamese network for real time object tracking"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "security of deep learning based lane keeping system under physical world adversarial attack"):
  with members:
    - "a commute in data the comma2k19 dataset"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  with members:
    - "discretization based solutions for secure machine learning against adversarial attacks"
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "weight map layer for noise and adversarial attack robustness"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "simple black box adversarial attacks"

Connected through hyperedge HE-4 (induced by source paper "advhat real world adversarial attack on arcface face id system"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "adversarial patch"
    - "cosface large margin cosine loss for deep face recognition"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-5 (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"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "a twofold siamese network for real time object tracking"

Connected through hyperedge HE-6 (induced by source paper "adversarially robust few shot learning a meta learning approach"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "adversarial attacks on copyright detection systems"
    - "theoretically principled trade off between robustness and accuracy"
    - "a closer look at few shot classification"
    - "adversarially robust transfer learning"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "practical adversarial attack against object detector"

Connected through hyperedge HE-7 (induced by source paper "fooling detection alone is not enough first adversarial attack against multiple object tracking"):
  with members:
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "practical adversarial attack against object detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-8 (induced by source paper "robustness metrics for real world adversarial examples"):
  with members:
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "on evaluating adversarial robustness"
    - "relational inductive biases deep learning and graph networks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "physical adversarial examples for object detectors"
    - "robust physical world attacks on deep learning models"
    - "synthesizing robust adversarial examples"
    - "yolo9000 better faster stronger"

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)