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 "advpc transferable adversarial perturbations on 3d point clouds"):
  Abstract: Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and only recently extended to 3D point clouds. In this work, we present novel data-driven adversarial attacks against 3D point cloud networks. We aim to address the following problems in current 3D point cloud adversarial attacks: they do not transfer well between different networks, and they are easy to defend against simple statistical methods. To this extent, we develop new point cloud attacks (we dub AdvPC) that exploit input data distributions. These attacks lead to perturbations that are resilient against current defenses while remaining highly transferable compared to state-of-the-art attacks. We test our attacks using four popular point cloud networks: PointNet, PointNet++ (MSG and SSG), and DGCNN. Our proposed attack enables an increase in the transferability of up to 20 points for some networks. It also increases the ability to break defenses of up to 23 points on ModelNet40 data.

Includes members:
  Member "meshadv adversarial meshes for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing")
  Member "point cloud oversegmentation with graph structured deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grid gcn for fast and scalable point cloud learning")
  Member "cross domain transferability of adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial transferability in wearable sensor systems")
  Member "sada semantic adversarial diagnostic attacks for autonomous applications":
    also connected through other hyperedges:
      (none)
  Member "generating 3d adversarial point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "pointcloud saliency maps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks")
  Member "dynamic graph cnn for learning on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "strike with a pose neural networks are easily fooled by strange poses of familiar objects":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")

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)