human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "commandersong a systematic approach for practical adversarial voice recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "security vetting process of smart home assistant applications a first look and case studies")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "feature denoising for improving adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "deep learning for generic object detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "realistic adversarial examples in 3d meshes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "you only look twice rapid multi scale object detection in satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep active learning for remote sensing object detection")

Question: Please tell me which class the source paper 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)