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: "pseudo lidar accurate depth for 3d object detection in autonomous driving"
  Abstract: Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images, there is still a notable performance gap. In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation. Concretely, we adapt the stereo network architecture and loss function to be more aligned with accurate depth estimation of far away objects (currently the primary weakness of pseudo-LiDAR). Further, we explore the idea to leverage cheaper but extremely sparse LiDAR sensors, which alone provide insufficient information for 3D detection, to de-bias our depth estimation. We propose a depth-propagation algorithm, guided by the initial depth estimates, to diffuse these few exact measurements across the entire depth map. We show on the KITTI object detection benchmark that our combined approach yields substantial improvements in depth estimation and stereo-based 3D object detection --- outperforming the previous state-of-the-art detection accuracy for far-away objects by 40%. Our code will be publicly 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"
    - "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 "monolayout amodal scene layout from a single image"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "wise ale wide sample estimator for approximate latent embedding"
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "probabilistic object detection definition and evaluation"

Connected through hyperedge HE-3 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation"):
  with members:
    - "dsgn deep stereo geometry network for 3d object detection"
    - "canadian adverse driving conditions dataset"
    - "object centric stereo matching for 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pv rcnn point voxel feature set abstraction for 3d object detection"
    - "task aware monocular depth estimation for 3d object detection"
    - "scalability in perception for autonomous driving waymo open dataset"
    - "triangulation learning network from monocular to stereo 3d object detection"

Connected through hyperedge HE-4 (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"
    - "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "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-5 (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"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deep fitting degree scoring network for monocular 3d object detection"

Connected through hyperedge HE-6 (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"
    - "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"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "single stage monocular 3d object detection with virtual cameras"):
  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"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-8 (induced by source paper "eventgan leveraging large scale image datasets for event cameras"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "events to video bringing modern computer vision to event cameras"
    - "on the variance of the adaptive learning rate and beyond"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "yolov3 an incremental improvement"

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)