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: "a general pipeline for 3d detection of vehicles"
  Abstract: Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D detection networks. To identify the 3D box, an effective model fitting algorithm is developed based on generalised car models and score maps. A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box. This pipeline is tested on the KITTI dataset using two different 2D detection networks. The 3D detection results based on these two networks are similar, demonstrating the flexibility of the proposed pipeline. The results rank second among the 3D detection algorithms, indicating its competencies in 3D detection.

Connected through hyperedge HE-1 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "associatively segmenting instances and semantics in point clouds"
    - "3d instance segmentation via multi task metric learning"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"

Connected through hyperedge HE-2 (induced by source paper "pseudo lidar accurate depth for 3d object detection in autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "dense depth posterior ddp from single image and sparse range"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "learning a layout transfer network for context aware object detection"):
  with members:
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "seeing what is not there learning context to determine where objects are missing"
    - "dsod learning deeply supervised object detectors from scratch"
    - "foveanet perspective aware urban scene parsing"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-4 (induced by source paper "m3d rpn monocular 3d region proposal network for object detection"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "deep feature pyramid reconfiguration for object detection"
    - "pedestrian detection with autoregressive network phases"
    - "localization recall precision lrp a new performance metric for object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"

Connected through hyperedge HE-5 (induced by source paper "sensor fusion for joint 3d object detection and semantic segmentation"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "pointpillars fast encoders for object detection from point clouds"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "deep layer aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-6 (induced by source paper "tanet robust 3d object detection from point clouds with triple attention"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "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"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "dssd deconvolutional single shot detector"

Connected through hyperedge HE-7 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "a robust pavement mapping system based on normal constrained stereo visual odometry"
    - "real time dense stereo embedded in a uav for road inspection"
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "scene understanding networks for autonomous driving based on around view monitoring system"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"

Connected through hyperedge HE-8 (induced by source paper "capturing object detection uncertainty in multi layer grid maps"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

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)