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: "depth reconstruction from sparse samples representation algorithm and sampling"
  Abstract: The rapid development of 3D technology and computer vision applications have motivated a thrust of methodologies for depth acquisition and estimation. However, most existing hardware and software methods have limited performance due to poor depth precision, low resolution and high computational cost. In this paper, we present a computationally efficient method to recover dense depth maps from sparse measurements. We make three contributions. First, we provide empirical evidence that depth maps can be encoded much more sparsely than natural images by using common dictionaries such as wavelets and contourlets. We also show that a combined wavelet-contourlet dictionary achieves better performance than using either dictionary alone. Second, we propose an alternating direction method of multipliers (ADMM) to achieve fast reconstruction. A multi-scale warm start procedure is proposed to speed up the convergence. Third, we propose a two-stage randomized sampling scheme to optimally choose the sampling locations, thus maximizing the reconstruction performance for any given sampling budget. Experimental results show that the proposed method produces high quality dense depth estimates, and is robust to noisy measurements. Applications to real data in stereo matching are demonstrated.

Connected through hyperedge HE-1 (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-2 (induced by source paper "image guided depth sampling and reconstruction"):
  with members:
    - "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"
    - "propagating confidences through cnns for sparse data regression"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "estimating depth from rgb and sparse sensing"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "deep convolutional compressed sensing for lidar depth completion"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-3 (induced by source paper "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"):
  with members:
    - "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"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "free form image inpainting with gated convolution"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-4 (induced by source paper "deep depth completion of a single rgb d image"):
  with members:
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "real time user guided image colorization with learned deep priors"
    - "matterport3d learning from rgb d data in indoor environments"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "sparsity invariant cnns"
    - "semantic scene completion from a single depth image"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional compressed sensing for lidar depth completion"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep component analysis via alternating direction neural networks"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual networks"
    - "semantically guided depth upsampling"

Connected through hyperedge HE-6 (induced by source paper "sparse to dense depth prediction from sparse depth samples and a single image"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "sparse depth sensing for resource constrained robots"
    - "semi supervised deep learning for monocular depth map prediction"
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-7 (induced by source paper "sparse depth sensing for resource constrained robots"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "compressed sensing for scalable robotic tactile skins"
    - "deeper depth prediction with fully convolutional residual networks"
    - "adaptive foveated single pixel imaging with dynamic super sampling"
    - "orb slam a versatile and accurate monocular slam system"
    - "high performance and tunable stereo reconstruction"
    - "stable image reconstruction using total variation minimization"
    - "robust sparse analysis regularization"

Connected through hyperedge HE-8 (induced by source paper "plug and play admm for image restoration fixed point convergence and applications"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "algorithm induced prior for image restoration"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"
    - "compressive imaging via approximate message passing with image denoising"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"

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)