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: "graph laplacian regularization for image denoising analysis in the continuous domain"
  Abstract: Inverse imaging problems are inherently underdetermined, and hence, it is important to employ appropriate image priors for regularization. One recent popular prior—the graph Laplacian regularizer—assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper, we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising. Specifically, we first show the convergence of the graph Laplacian regularizer to a continuous-domain functional, integrating a norm measured in a locally adaptive metric space. Focusing on image denoising, we derive an optimal metric space assuming non-local self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain. We then interpret graph Laplacian regularization as an anisotropic diffusion scheme to explain its behavior during iterations, e.g., its tendency to promote piecewise smooth signals under certain settings. To verify our analysis, an iterative image denoising algorithm is developed. Experimental results show that our algorithm performs competitively with state-of-the-art denoising methods, such as BM3D for natural images, and outperforms them significantly for piecewise smooth images.

Connected through hyperedge HE-1 (induced by source paper "fast graph metric learning via gershgorin disc alignment"):
  with members:
    - "feature graph learning for 3d point cloud denoising"
    - "graph based blind image deblurring from a single photograph"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-2 (induced by source paper "joint learning of graph representation and node features in graph convolutional neural networks"):
  with members:
    - "gmnn graph markov neural networks"
    - "adaptive graph convolutional neural networks"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "a graph cnn for 3d point cloud classification"
    - "dynamic graph cnn for learning on point clouds"
    - "non local neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-3 (induced by source paper "feature graph learning for 3d point cloud denoising"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graph based blind image deblurring from a single photograph"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Connected through hyperedge HE-4 (induced by source paper "error analysis on graph laplacian regularized estimator"):
  with members:
    - "alternating linear bandits for online matrix factorization recommendation"
    - "network lasso clustering and optimization in large graphs"
    - "matrix completion on graphs"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "consistency of trace norm minimization"

Connected through hyperedge HE-5 (induced by source paper "graph based blind image deblurring from a single photograph"):
  with members:
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "blind image deblurring via reweighted graph total variation"
    - "the little engine that could regularization by denoising red"
    - "bi l0 l2 norm regularization for blind motion deblurring"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "spectral domain sampling of graph signals"):
  with members:
    - "blind identification of graph filters"
    - "spectral projector based graph fourier transforms"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"
    - "subgraph based filterbanks for graph signals"

Connected through hyperedge HE-7 (induced by source paper "deep graph laplacian regularization for robust denoising of real images"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "learning to see in the dark"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "optnet differentiable optimization as a layer in neural networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-8 (induced by source paper "zoom and learn generalizing deep stereo matching to novel domains"):
  with members:
    - "self supervised learning for stereo matching with self improving ability"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "demon depth and motion network for learning monocular stereo"
    - "semi supervised deep learning for monocular depth map prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

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)