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: "3d fully convolutional network for vehicle detection in point cloud"
  Abstract: 2D fully convolutional network has been recently successfully applied to object detection from images. In this paper, we extend the fully convolutional network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows a significant performance improvement over the previous point cloud based detection approaches.

Connected through hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

Connected through hyperedge HE-2 (induced by source paper "smoke single stage monocular 3d object detection via keypoint estimation"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "objects as points"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"

Connected through hyperedge HE-3 (induced by source paper "pointtracknet an end to end network for 3 d object detection and tracking from point clouds"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "pointit a fast tracking framework based on 3d instance segmentation"
    - "autotrack a lightweight object detection and tracking system for the sae autodrive challenge"
    - "fantrack 3d multi object tracking with feature association network"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "flownet3d learning scene flow in 3d point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "focal loss in 3d object detection"

Connected through hyperedge HE-4 (induced by source paper "pugeo net a geometry centric network for 3d point cloud upsampling"):
  with members:
    - "pu gan a point cloud upsampling adversarial network"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "residual dense network for image super resolution"
    - "ec net an edge aware point set consolidation network"
    - "pu net point cloud upsampling network"
    - "large scale point cloud semantic segmentation with superpoint graphs"

Connected through hyperedge HE-5 (induced by source paper "3dssd point based 3d single stage object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "3d object detection on point clouds using local ground aware and adaptive representation of scenes surface"):
  with members:
    - "pointpillars fast encoders for object detection from point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "cnn for very fast ground segmentation in velodyne lidar data"
    - "ssd single shot multibox detector"
    - "multi view 3d object detection network for autonomous driving"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "temporal pulses driven spiking neural network for fast object recognition in autonomous driving"):
  with members:
    - "supervised learning based on temporal coding in spiking neural networks"
    - "multi view 3d object detection network for autonomous driving"
    - "hfirst a temporal approach to object recognition"

Connected through hyperedge HE-8 (induced by source paper "learning object bounding boxes for 3d instance segmentation on point clouds"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "deep hough voting for 3d object detection in point clouds"

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)