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: "dense depth posterior ddp from single image and sparse range"
  Abstract: We present a deep learning system to infer the posterior distribution of a dense depth map associated with an image, by exploiting sparse range measurements, for instance from a lidar. While the lidar may provide a depth value for a small percentage of the pixels, we exploit regularities reflected in the training set to complete the map so as to have a probability over depth for each pixel in the image. We exploit a Conditional Prior Network, that allows associating a probability to each depth value given an image, and combine it with a likelihood term that uses the sparse measurements. Optionally we can also exploit the availability of stereo during training, but in any case only require a single image and a sparse point cloud at run-time. We test our approach on both unsupervised and supervised depth completion using the KITTI benchmark, and improve the state-of-the-art in both.

Connected through hyperedge HE-1 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "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-2 (induced by source paper "pseudo lidar accurate depth for 3d object detection in autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "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"
    - "a general pipeline for 3d detection of vehicles"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "deep rgb d canonical correlation analysis for sparse depth completion"):
  with members:
    - "depth coefficients for depth completion"
    - "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning depth with convolutional spatial propagation network"
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"
    - "sparse and dense data with cnns depth completion and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning guided convolutional network for depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "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"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"

Connected through hyperedge HE-5 (induced by source paper "unsupervised depth completion from visual inertial odometry"):
  with members:
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "geo supervised visual depth prediction"

Connected through hyperedge HE-6 (induced by source paper "geonet deep geodesic networks for point cloud analysis"):
  with members:
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "conditional prior networks for optical flow"
    - "geo supervised visual depth prediction"
    - "pcpnet learning local shape properties from raw point clouds"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-7 (induced by source paper "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sbnet sparse blocks network for fast inference"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"

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)