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 dynamic point cloud denoising via spatio temporal graph modeling"):
  Abstract: The prevalence of accessible depth sensing and 3D laser scanning techniques has enabled the convenient acquisition of 3D dynamic point clouds, which provide efficient representation of arbitrarily-shaped objects in motion. Nevertheless, dynamic point clouds are often perturbed by noise due to hardware, software or other causes. While many methods have been proposed for the denoising of static point clouds, dynamic point cloud denoising has not been studied in the literature yet. Hence, we address this problem based on the proposed spatio-temporal graph modeling, exploiting both the intra-frame similarity and inter-frame consistency. Specifically, we first represent a point cloud sequence on graphs and model it via spatio-temporal Gaussian Markov Random Fields on defined patches. Then for each target patch, we pose a Maximum a Posteriori estimation, and propose the corresponding likelihood and prior functions via spectral graph theory, leveraging its similar patches within the same frame and corresponding patch in the previous frame. This leads to our problem formulation, which jointly optimizes the underlying dynamic point cloud and spatio-temporal graph. Finally, we propose an efficient algorithm for patch construction, similar/corresponding patch search, intra- and inter-frame graph construction, and the optimization of our problem formulation via alternating minimization. Experimental results show that the proposed method outperforms frame-by-frame denoising from state-of-the-art static point cloud denoising approaches.

Includes members:
  Member "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")

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)