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 "an efficient tensor completion method via new latent nuclear norm"):
  Abstract: In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme. To overcome this drawback, a new latent nuclear norm equipped with a more balanced unfolding scheme is defined for low-rank regularizer. Moreover, the new latent nuclear norm together with the Frank-Wolfe (FW) algorithm is developed as an efficient completion method by utilizing the sparsity structure of observed tensor. Specifically, both FW linear subproblem and line search only need to access the observed entries, by which we can instead maintain the sparse tensors and a set of small basis matrices during iteration. Most operations are based on sparse tensors, and the closed-form solution of FW linear subproblem can be obtained from rank-one SVD. We theoretically analyze the space-complexity and time-complexity of the proposed method, and show that it is much more efficient over other norm-based completion methods for higher-order tensors. Extensive experimental results of visual-data inpainting demonstrate that the proposed method is able to achieve state-of-the-art performance at smaller costs of time and space, which is very meaningful for the memory-limited equipment in practical applications.

Includes members:
  Member "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provable tensor ring completion")
  Member "higher dimension tensor completion via low rank tensor ring decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tensor grid decomposition with application to tensor completion")
  Member "high order tensor completion for data recovery via sparse tensor train optimization":
    also connected through other hyperedges:
      (none)
  Member "efficient tensor completion for color image and video recovery low rank tensor train":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low rank tensor grid for image completion")
  Member "efficient low rank tensor ring completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor grid for image completion")
  Member "bayesian robust tensor factorization for incomplete multiway data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bayesian tensorized neural networks with automatic rank selection")
  Member "tensor ring decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution")
  Member "smooth parafac decomposition for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable model for tensor ring completion")

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)