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 "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps"):
  Abstract: The ability to reliably perceive the environmental states, particularly the existence of objects and their motion behavior, is crucial for autonomous driving. In this work, we propose an efficient deep model, called MotionNet, to jointly perform perception and motion prediction from 3D point clouds. MotionNet takes a sequence of LiDAR sweeps as input and outputs a bird's eye view (BEV) map, which encodes the object category and motion information in each grid cell. The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses. Extensive experiments show that the proposed method overall outperforms the state-of-the-arts, including the latest scene-flow- and 3D-object-detection-based methods. This indicates the potential value of the proposed method serving as a backup to the bounding-box-based system, and providing complementary information to the motion planner in autonomous driving. Code is available at https://github.com/pxiangwu/MotionNet.

Includes members:
  Member "3d point cloud processing and learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction")
  Member "point cloud processing via recurrent set encoding":
    also connected through other hyperedges:
      (none)
  Member "from points to parts 3d object detection from point cloud with part aware and part aggregation network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d object detection from lidar data using distance depended feature extraction")
  Member "sr lstm state refinement for lstm towards pedestrian trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle")
  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 "multi agent tensor fusion for contextual trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grid gcn for fast and scalable point cloud learning")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)