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: "patch refinement localized 3d object detection"
  Abstract: We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data. Patch Refinement is composed of two independently trained Voxelnet-based networks, a Region Proposal Network (RPN) and a Local Refinement Network (LRN). We decompose the detection task into a preliminary Bird's Eye View (BEV) detection step and a local 3D detection step. Based on the proposed BEV locations by the RPN, we extract small point cloud subsets ("patches"), which are then processed by the LRN, which is less limited by memory constraints due to the small area of each patch. Therefore, we can apply encoding with a higher voxel resolution locally. The independence of the LRN enables the use of additional augmentation techniques and allows for an efficient, regression focused training as it uses only a small fraction of each scene. Evaluated on the KITTI 3D object detection benchmark, our submission from January 28, 2019, outperformed all previous entries on all three difficulties of the class car, using only 50 % of the available training data and only LiDAR information.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "pv rcnn point voxel feature set abstraction for 3d object detection"):
  with members:
    - "multi view pointnet for 3d scene understanding"
    - "std sparse to dense 3d object detector for point cloud"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "starnet targeted computation for object detection in point clouds"
    - "pixor real time 3d object detection from point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "iou loss for 2d 3d object detection"

Connected through hyperedge HE-3 (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"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "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"
    - "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"

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)