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 "multi view registration based on weighted low rank and sparse matrix decomposition of motions"):
  Abstract: Recently, the low rank and sparse (LRS) matrix decomposition has been introduced as an effective mean to solve the multi-view registration. It views each available relative motion as a block element to reconstruct one matrix so as to approximate the low rank matrix, where global motions can be recovered for multi-view registration. However, this approach is sensitive to the sparsity of the reconstructed matrix and it treats all block elements equally in spite of their varied reliability. Therefore, this paper proposes an effective approach for multi-view registration by the weighted LRS decomposition. Based on the anti-symmetry property of relative motions, it firstly proposes a completion strategy to reduce the sparsity of the reconstructed matrix. The reduced sparsity of reconstructed matrix can improve the robustness of LRS decomposition. Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability. By introducing the weight, more accurate registration results can be recovered from the estimated low rank matrix with good efficiency. Experimental results tested on public data sets illustrate the superiority of the proposed approach over the state-of-the-art approaches on robustness, accuracy, and efficiency.

Includes members:
  Member "weighted motion averaging for the registration of multi view range scans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "registration of multi view point sets under the perspective of expectation maximization")
  Member "outlier robust icp for minimizing fractional rmsd":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences")

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)