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 "category wise attack transferable adversarial examples for anchor free object detection"):
  Abstract: Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: sutle perturbations can completely change the classification results. Their vulnerability has led to a surge of research in this direction. However, most works dedicated to attacking anchor-based object detection models. In this work, we aim to present an effective and efficient algorithm to generate adversarial examples to attack anchor-free object models based on two approaches. First, we conduct category-wise instead of instance-wise attacks on the object detectors. Second, we leverage the high-level semantic information to generate the adversarial examples. Surprisingly, the generated adversarial examples it not only able to effectively attack the targeted anchor-free object detector but also to be transferred to attack other object detectors, even anchor-based detectors such as Faster R-CNN.

Includes members:
  Member "curls whey boosting black box adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "improving adversarial robustness via promoting ensemble diversity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "evading defenses to transferable adversarial examples by translation invariant attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "objects as points":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "bottom up object detection by grouping extreme and center points":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental few shot object detection")
  Member "minimally distorted adversarial examples with a fast adaptive boundary attack":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks")
  Member "robust adversarial perturbation on deep proposal based models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards adversarially robust 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)