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: "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
  Abstract: In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.

Connected through hyperedge HE-1 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "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"
    - "learning depth with convolutional spatial propagation network"
    - "segstereo exploiting semantic information for disparity estimation"

Connected through hyperedge HE-2 (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"
    - "dense depth posterior ddp from single image and sparse range"
    - "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-3 (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:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "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"
    - "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-4 (induced by source paper "generating and exploiting probabilistic monocular depth estimates"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-5 (induced by source paper "listereo generate dense depth maps from lidar and stereo imagery"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "estimating depth from rgb and sparse sensing"
    - "pyramid stereo matching network"
    - "in defense of classical image processing fast depth completion on the cpu"

Connected through hyperedge HE-6 (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"
    - "dense depth posterior ddp from single image and sparse range"
    - "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-7 (induced by source paper "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "real time dense depth estimation by fusing stereo with sparse depth measurements"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "fast autonomous flight in gps denied and cluttered environments"

Connected through hyperedge HE-8 (induced by source paper "unsupervised depth completion from visual inertial odometry"):
  with members:
    - "dense depth posterior ddp from single image and sparse range"
    - "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"

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)