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: "from rank estimation to rank approximation rank residual constraint for image denoising"
  Abstract: In this paper, we propose a novel approach for the rank minimization problem, termed rank residual constraint (RRC). Different from existing low-rank based approaches, such as the well-known weighted nuclear norm minimization (WNNM) and nuclear norm minimization (NNM), which aim to estimate the underlying low-rank matrix directly from the corrupted observation, we progressively approximate or approach the underlying low-rank matrix via minimizing the rank residual. By integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we develop an iterative algorithm for image denoising. To this end, we first present a recursive based nonlocal means method to obtain a good reference of the original image patch groups, and then the rank residual of the image patch groups between this reference and the noisy image is minimized to achieve a better estimate of the desired image. In this manner, both the reference and the estimated image in each iteration are improved gradually and jointly. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art denoising methods in both the objective and perceptual qualities.

Connected through hyperedge HE-1 (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"
    - "group sparsity residual with non local samples for image denoising"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "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)