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: "disentangling monocular 3d object detection"
  Abstract: In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes. Our proposed loss disentanglement has the twofold advantage of simplifying the training dynamics in the presence of losses with complex interactions of parameters, and sidestepping the issue of balancing independent regression terms. Our solution overcomes these issues by isolating the contribution made by groups of parameters to a given loss, without changing its nature. We further apply loss disentanglement to another novel, signed Intersection-over-Union criterion-driven loss for improving 2D detection results. Besides our methodological innovations, we critically review the AP metric used in KITTI3D, which emerged as the most important dataset for comparing 3D detection results. We identify and resolve a flaw in the 11-point interpolated AP metric, affecting all previously published detection results and particularly biases the results of monocular 3D detection. We provide extensive experimental evaluations and ablation studies on the KITTI3D and nuScenes datasets, setting new state-of-the-art results on object category car by large margins.

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"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "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 "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "albumentations fast and flexible image augmentations"
    - "how much position information do convolutional neural networks encode"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"
    - "canonical surface mapping via geometric cycle consistency"

Connected through hyperedge HE-3 (induced by source paper "cps class level 6d pose and shape estimation from monocular images"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "multi path learning for object pose estimation across domains"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mesh r cnn"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "fcos fully convolutional one stage object detection"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "objects as points"

Connected through hyperedge HE-5 (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"
    - "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"
    - "end to end multi view fusion for 3d object detection in lidar point clouds"

Connected through hyperedge HE-6 (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"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "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-7 (induced by source paper "nuscenes a multimodal dataset for autonomous driving"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "starnet targeted computation for object detection in point clouds"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "a baseline for 3d multi object tracking"
    - "physics based rendering for improving robustness to rain"
    - "the apolloscape open dataset for autonomous driving and its application"

Connected through hyperedge HE-8 (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"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deep fitting degree scoring network for monocular 3d object detection"

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)