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: "deep graph laplacian regularization for robust denoising of real images"
  Abstract: Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise. In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising. Specifically, by integrating graph Laplacian regularization as a trainable module into a deep learning framework, we are less susceptible to overfitting than pure CNN-based approaches, achieving higher robustness to small datasets and cross-domain denoising. First, a sparse neighborhood graph is built from the output of a convolutional neural network (CNN). Then the image is restored by solving an unconstrained quadratic programming problem, using a corresponding graph Laplacian regularizer as a prior term. The proposed restoration pipeline is fully differentiable and hence can be end-to-end trained. Experimental results demonstrate that our work is less prone to overfitting given small training data. It is also endowed with strong cross-domain generalization power, outperforming the state-of-the-art approaches by a remarkable margin.

Connected through hyperedge HE-1 (induced by source paper "robust deep graph based learning for binary classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "graph laplacian regularized graph convolutional networks for semi supervised learning"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "dimensionality driven learning with noisy labels"
    - "dynamic graph cnn for learning on point clouds"
    - "learning to reweight examples for robust deep learning"
    - "iterative learning with open set noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-2 (induced by source paper "deep surface normal estimation with hierarchical rgb d fusion"):
  with members:
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "noise2noise learning image restoration without clean data"
    - "deformable shape completion with graph convolutional autoencoders"

Connected through hyperedge HE-3 (induced by source paper "discrete laplace operator estimation for dynamic 3d reconstruction"):
  with members:
    - "automatic adaptation of person association for multiview tracking in group activities"
    - "learning monocular 3d human pose estimation from multi view images"
    - "convolutional pose machines"
    - "panoptic studio a massively multiview system for social interaction capture"
    - "self expressive dictionary learning for dynamic 3d reconstruction"
    - "fast and robust archetypal analysis for representation learning"

Connected through hyperedge HE-4 (induced by source paper "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "from rank estimation to rank approximation rank residual constraint for image denoising"
    - "group sparsity residual with non local samples for image denoising"
    - "fast resampling of three dimensional point clouds via graphs"
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

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)