human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"):
  Abstract: 3D point cloud - a new signal representation of volumetric objects - is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud - e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors - imply non-negligible noise in the data. In this paper, we adopt a previously proposed low-dimensional manifold model for the surface patches in the point cloud and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer has a natural graph spectral interpretation, and has desirable numerical stability properties via eigenanalysis. Extensive simulation results show that our proposed denoising scheme can outperform state-of-the-art methods in objective metrics and can better preserve visually salient structural features like edges.

Includes members:
  Member "deep surface normal estimation with hierarchical rgb d fusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data")
  Member "from rank estimation to rank approximation rank residual constraint for image denoising":
    also connected through other hyperedges:
      (none)
  Member "group sparsity residual with non local samples for image denoising":
    also connected through other hyperedges:
      (none)
  Member "deep graph laplacian regularization for robust denoising of real images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust deep graph based learning for binary classification")
  Member "fast resampling of three dimensional point clouds via graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "image denoising using group sparsity residual and external nonlocal self similarity prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kernel free image deblurring with a pair of blurred noisy images")
  Member "graph laplacian regularization for image denoising analysis in the continuous domain":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast graph metric learning via gershgorin disc alignment")
  Member "random walk graph laplacian based smoothness prior for soft decoding of jpeg images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")

Question: Please tell me which class the source paper 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)