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 "low rank tensor grid for image completion"):
  Abstract: Tensor completion estimates missing components by exploiting the low-rank structure of multi-way data. The recently proposed methods based on tensor train (TT) and tensor ring (TR) show better performance in image recovery than classical ones. Compared with TT and TR, the projected entangled pair state (PEPS), which is also called tensor grid (TG), allows more interactions between different dimensions, and may lead to more compact representation. In this paper, we propose to perform image completion based on low-rank tensor grid. A two-stage density matrix renormalization group algorithm is used for initialization of TG decomposition, which consists of multiple TT decompositions. The latent TG factors can be alternatively obtained by solving alternating least squares problems. To further improve the computational efficiency, a multi-linear matrix factorization for low rank TG completion is developed by using parallel matrix factorization. Experimental results on synthetic data and real-world images show the proposed methods outperform the existing ones in terms of recovery accuracy.

Includes members:
  Member "provable model for tensor ring completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provable tensor ring completion")
  Member "low rank tensor completion for multiway visual data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations from imperfect time series data via tensor rank regularization")
  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 "provable tensor ring completion")
  Member "efficient low rank tensor ring completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provable tensor ring completion")
  Member "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coupled tensor completion via low rank tensor ring")
  Member "an iterative reweighted method for tucker decomposition of incomplete tensors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable model for tensor ring completion")
  Member "tensor completion by alternating minimization under the tensor train tt model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")

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.LG(Machine Learning)