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: "ipod intensive point based object detector for point cloud"
  Abstract: We present a novel 3D object detection framework, named IPOD, based on raw point cloud. It seeds object proposal for each point, which is the basic element. This paradigm provides us with high recall and high fidelity of information, leading to a suitable way to process point cloud data. We design an end-to-end trainable architecture, where features of all points within a proposal are extracted from the backbone network and achieve a proposal feature for final bounding inference. These features with both context information and precise point cloud coordinates yield improved performance. We conduct experiments on KITTI dataset, evaluating our performance in terms of 3D object detection, Bird's Eye View (BEV) detection and 2D object detection. Our method accomplishes new state-of-the-art , showing great advantage on the hard set.

Connected through hyperedge HE-1 (induced by source paper "hvnet hybrid voxel network for lidar based 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"
    - "pixor real time 3d object detection from point clouds"
    - "disentangling monocular 3d object detection"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "iou loss for 2d 3d object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"

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"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "monocular 3d object detection with pseudo lidar point cloud"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "3d object detection using scale invariant and feature reweighting networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"

Connected through hyperedge HE-4 (induced by source paper "fast point r cnn"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "pointpillars fast encoders for object detection from point clouds"
    - "yolo3d end to end real time 3d oriented object bounding box detection from lidar point cloud"
    - "roarnet a robust 3d object detection based on region approximation refinement"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "complex yolo real time 3d object detection on point clouds"

Connected through hyperedge HE-5 (induced by source paper "starnet targeted computation for object detection in point clouds"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "end to end multi view fusion for 3d object detection in lidar point clouds"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "pointpillars fast encoders for object detection from point clouds"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-6 (induced by source paper "adaptive and azimuth aware fusion network of multimodal local features for 3d object detection"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "roarnet a robust 3d object detection based on region approximation refinement"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "cbam convolutional block attention module"
    - "orthographic feature transform for monocular 3d object detection"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-7 (induced by source paper "pointpainting sequential fusion for 3d object detection"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "class balanced grouping and sampling for point cloud 3d object detection"

Connected through hyperedge HE-8 (induced by source paper "rotation invariant point cloud classification where local geometry meets global topology"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "revisiting point cloud classification a new benchmark dataset and classification model on real world data"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "multi kernel diffusion cnns for graph based learning on point clouds"
    - "local spectral graph convolution for point set feature learning"

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)