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: "practical adversarial attack against object detector"
  Abstract: In this paper, we proposed the first practical adversarial attacks against object detectors in realistic situations: the adversarial examples are placed in different angles and distances, especially in the long distance (over 20m) and wide angles 120 degree. To improve the robustness of adversarial examples, we proposed the nested adversarial examples and introduced the image transformation techniques. Transformation methods aim to simulate the variance factors such as distances, angles, illuminations, etc., in the physical world. Two kinds of attacks were implemented on YOLO V3, a state-of-the-art real-time object detector: hiding attack that fools the detector unable to recognize the object, and appearing attack that fools the detector to recognize the non-existent object. The adversarial examples are evaluated in three environments: indoor lab, outdoor environment, and the real road, and demonstrated to achieve the success rate up to 92.4% based on the distance range from 1m to 25m. In particular, the real road testing of hiding attack on a straight road and a crossing road produced the success rate of 75% and 64% respectively, and the appearing attack obtained the success rates of 63% and 81% respectively, which we believe, should catch the attention of the autonomous driving community.

Connected through hyperedge HE-1 (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-2 (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"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-3 (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"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-4 (induced by source paper "robust assessment of real world adversarial examples"):
  with members:
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "on evaluating adversarial robustness"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "relational inductive biases deep learning and graph networks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "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)