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: "sparse and noisy lidar completion with rgb guidance and uncertainty"
  Abstract: This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped to a 2D plane. We propose a new framework which extracts both global and local information in order to produce proper depth maps. We argue that simple depth completion does not require a deep network. However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input. This improves the accuracy significantly. Moreover, confidence masks are exploited in order to take into account the uncertainty in the depth predictions from each modality. This fusion method outperforms the state-of-the-art and ranks first on the KITTI depth completion benchmark. Our code with visualizations is available.

Connected through hyperedge HE-1 (induced by source paper "don t forget the past recurrent depth estimation from monocular video"):
  with members:
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "dynamonet dynamic action and motion network"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

Connected through hyperedge HE-2 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction"):
  with members:
    - "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "learning single camera depth estimation using dual pixels"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "uncertainty estimation in one stage object detection"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "gated2depth real time dense lidar from gated images"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"

Connected through hyperedge HE-3 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "depth coefficients for depth completion"
    - "depth completion from sparse lidar data with depth normal constraints"
    - "3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization"
    - "deep rgb d canonical correlation analysis for sparse depth completion"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "learning depth with convolutional spatial propagation network"
    - "segstereo exploiting semantic information for disparity estimation"

Connected through hyperedge HE-4 (induced by source paper "fis nets full image supervised networks for monocular depth estimation"):
  with members:
    - "conf net predicting depth completion error map for high confidence dense 3d point cloud"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "digging into self supervised monocular depth estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-5 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-6 (induced by source paper "to complete or to estimate that is the question a multi task approach to depth completion and monocular depth estimation"):
  with members:
    - "confidence propagation through cnns for guided sparse depth regression"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

Connected through hyperedge HE-7 (induced by source paper "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"):
  with members:
    - "deep attention based classification network for robust depth prediction"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"

Connected through hyperedge HE-8 (induced by source paper "cspn learning context and resource aware convolutional spatial propagation networks for depth completion"):
  with members:
    - "learning guided convolutional network for depth completion"
    - "deep high resolution representation learning for human pose estimation"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"

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)