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: "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
  Abstract: In this work, we propose a novel method termed Frustum ConvNet (F-ConvNet) for amodal 3D object detection from point clouds. Given 2D region proposals in a RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustumlevel feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustumlevel features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of L-ConvNet, including a FCN variant that extracts multi-resolution frustum features, and a refined use of L-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. LConvNet assumes no prior knowledge of the working 3D environment, and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. LConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. We will make the code of L-ConvNet publicly available.

Connected through hyperedge HE-1 (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"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "objects as points"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (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"
    - "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"
    - "end to end multi view fusion for 3d object detection in lidar point clouds"

Connected through hyperedge HE-3 (induced by source paper "sianms non maximum suppression with siamese networks for multi camera 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "scalability in perception for autonomous driving waymo open dataset"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "single stage monocular 3d object detection with virtual cameras"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "birdnet a 3d object detection framework from lidar information"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "deep learning on radar centric 3d object detection"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pu gan a point cloud upsampling adversarial network"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-7 (induced by source paper "learning depth guided convolutions for monocular 3d object detection"):
  with members:
    - "disentangling monocular 3d object detection"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "shift r cnn deep monocular 3d object detection with closed form geometric constraints"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"
    - "learning guided convolutional network for depth completion"

Connected through hyperedge HE-8 (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"
    - "3d object detection using scale invariant and feature reweighting networks"
    - "ipod intensive point based object detector for point cloud"
    - "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"

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)