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: "semantically guided depth upsampling"
  Abstract: We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth in- terpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines glob- ally consistent solutions and preserves fine details and sharp depth bound- aries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.

Connected through hyperedge HE-1 (induced by source paper "dense depth posterior ddp from single image and sparse range"):
  with members:
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "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"

Connected through hyperedge HE-2 (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"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sparse to continuous enhancing monocular depth estimation using occupancy maps"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "j mod 2 joint monocular obstacle detection and depth estimation"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "sparsity invariant cnns"

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"
    - "dense depth posterior ddp from single image and sparse range"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unsupervised depth completion from visual inertial odometry"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "sparse and noisy lidar completion with rgb guidance and uncertainty"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "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"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-7 (induced by source paper "noise aware unsupervised deep lidar stereo fusion"):
  with members:
    - "open world stereo video matching with deep rnn"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "pyramid stereo matching network"
    - "deep convolutional compressed sensing for lidar depth completion"
    - "self supervised learning for stereo matching with self improving ability"
    - "sparsity invariant cnns"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-8 (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"
    - "dense depth posterior ddp from single image and sparse range"
    - "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"

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)