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: "monocular 3d object detection with pseudo lidar point cloud"
  Abstract: Monocular 3D scene understanding tasks, such as object size estimation, heading angle estimation and 3D localization, is challenging. Successful modern day methods for 3D scene understanding require the use of a 3D sensor. On the other hand, single image based methods have significantly worse performance. In this work, we aim at bridging the performance gap between 3D sensing and 2D sensing for 3D object detection by enhancing LiDAR-based algorithms to work with single image input. Specifically, we perform monocular depth estimation and lift the input image to a point cloud representation, which we call pseudo-LiDAR point cloud. Then we can train a LiDAR-based 3D detection network with our pseudo-LiDAR end-to-end. Following the pipeline of two-stage 3D detection algorithms, we detect 2D object proposals in the input image and extract a point cloud frustum from the pseudo-LiDAR for each proposal. Then an oriented 3D bounding box is detected for each frustum. To handle the large amount of noise in the pseudo-LiDAR, we propose two innovations: (1) use a 2D-3D bounding box consistency constraint, adjusting the predicted 3D bounding box to have a high overlap with its corresponding 2D proposal after projecting onto the image; (2) use the instance mask instead of the bounding box as the representation of 2D proposals, in order to reduce the number of points not belonging to the object in the point cloud frustum. Through our evaluation on the KITTI benchmark, we achieve the top-ranked performance on both bird's eye view and 3D object detection among all monocular methods, effectively quadrupling the performance over previous state-of-the-art. Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 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"
    - "depth coefficients for depth completion"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "hierarchical deep stereo matching on high resolution images"
    - "dpsnet end to end deep plane sweep stereo"
    - "triangulation learning network from monocular to stereo 3d object detection"

Connected through hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "grip graph based interaction aware trajectory prediction"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "dual graph convolutional network for semantic segmentation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-4 (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"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "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-5 (induced by source paper "monocular 3d object detection in cylindrical images from fisheye cameras"):
  with members:
    - "spherical view synthesis for self supervised 360 depth estimation"
    - "disentangling monocular 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "unsupervised domain adaptation through self supervision"
    - "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"
    - "unsupervised learning of depth and ego motion from cylindrical panoramic video"
    - "reprojection r cnn a fast and accurate object detector for 360 images"
    - "sweepnet wide baseline omnidirectional depth estimation"

Connected through hyperedge HE-6 (induced by source paper "zoomnet part aware adaptive zooming neural network for 3d object detection"):
  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"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"
    - "densefusion 6d object pose estimation by iterative dense fusion"

Connected through hyperedge HE-7 (induced by source paper "refinedmpl refined monocular pseudolidar for 3d object detection in autonomous driving"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "panoptic segmentation"
    - "deep optics for monocular depth estimation and 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "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"
    - "deep fitting degree scoring network for monocular 3d object detection"

Connected through hyperedge HE-8 (induced by source paper "learning depth guided convolutions for monocular 3d object detection"):
  with members:
    - "disentangling monocular 3d object detection"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "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"

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)