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: "cooper cooperative perception for connected autonomous vehicles based on 3d point clouds"
  Abstract: Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies.

Connected through hyperedge HE-1 (induced by source paper "f cooper feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "cascade r cnn delving into high quality object detection"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "cooperative perception for 3d object detection in driving scenarios using infrastructure sensors"):
  with members:
    - "trupercept trust modelling for autonomous vehicle cooperative perception from synthetic data"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "birdnet a 3d object detection framework from lidar information"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "carla an open urban driving simulator"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-3 (induced by source paper "trupercept trust modelling for autonomous vehicle cooperative perception from synthetic data"):
  with members:
    - "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
    - "bayesod a bayesian approach for uncertainty estimation in deep object detectors"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "carla an open urban driving simulator"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-4 (induced by source paper "low latency high level data sharing for connected and autonomous vehicular networks"):
  with members:
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "a probabilistic graph model for trust opinion estimation in online social networks"):
  with members:
    - "deepmove learning place representations through large scale movement data"
    - "who will you share a ride with factors that influence trust of potential rideshare partners"

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)