human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
  Abstract: 3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part-$A^2$ net). The whole framework consists of the part-aware stage and the part-aggregation stage. Firstly, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part-$A^2$ net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data.

Connected through hyperedge HE-1 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "point cloud processing via recurrent set encoding"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "meteornet deep learning on dynamic 3d point cloud sequences"

Connected through hyperedge HE-2 (induced by source paper "3d object detection from lidar data using distance depended feature extraction"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "range adaptation for 3d object detection in lidar"
    - "a baseline for 3d multi object tracking"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "3dssd point based 3d single stage object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "patch refinement localized 3d object detection"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fcos fully convolutional one stage object detection"
    - "fast point r cnn"
    - "deep hough voting for 3d object detection in point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"

Connected through hyperedge HE-4 (induced by source paper "zoomnet part aware adaptive zooming neural network for 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"
    - "densefusion 6d object pose estimation by iterative dense fusion"

Connected through hyperedge HE-5 (induced by source paper "pointrgcn graph convolution networks for 3d vehicles detection refinement"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "leveraging shape completion for 3d siamese tracking"
    - "pixor real time 3d object detection from point clouds"
    - "patch refinement localized 3d object detection"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"
    - "fast point r cnn"
    - "deep closest point learning representations for point cloud registration"

Connected through hyperedge HE-6 (induced by source paper "class balanced grouping and sampling for point cloud 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "weight standardization"
    - "open3d a modern library for 3d data processing"
    - "pointpillars fast encoders for object detection from point clouds"
    - "multi task learning as multi objective optimization"

Question: Please tell me which class the center node 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)