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 "tanet robust 3d object detection from point clouds with triple attention"):
  Abstract: In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object,the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.

Includes members:
  Member "pixor real time 3d object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "a general pipeline for 3d detection of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network")
  Member "pointpillars fast encoders for object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review deep learning on 3d point clouds")
  Member "pointrcnn 3d object proposal generation and detection from point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "joint 3d proposal generation and object detection from view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "voxelnet end to end learning for point cloud based 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review deep learning on 3d point clouds")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "frustum pointnets for 3d object detection from rgb d data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d point clouds")

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)