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 "pseudo lidar accurate depth for 3d object detection in autonomous driving"):
  Abstract: Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images, there is still a notable performance gap. In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation. Concretely, we adapt the stereo network architecture and loss function to be more aligned with accurate depth estimation of far away objects (currently the primary weakness of pseudo-LiDAR). Further, we explore the idea to leverage cheaper but extremely sparse LiDAR sensors, which alone provide insufficient information for 3D detection, to de-bias our depth estimation. We propose a depth-propagation algorithm, guided by the initial depth estimates, to diffuse these few exact measurements across the entire depth map. We show on the KITTI object detection benchmark that our combined approach yields substantial improvements in depth estimation and stereo-based 3D object detection --- outperforming the previous state-of-the-art detection accuracy for far-away objects by 40%. Our code will be publicly available at this https URL.

Includes members:
  Member "lasernet an efficient probabilistic 3d object detector for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "stereo r cnn based 3d object detection for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "sensor fusion for joint 3d object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "pixor real time 3d object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monolayout amodal scene layout from a single image")
  Member "dense depth posterior ddp from single image and sparse range":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "depth completion via deep basis fitting")
  Member "gs3d an efficient 3d object detection framework for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "a general pipeline for 3d detection of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network")

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)