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 "provable model for tensor ring completion"):
  Abstract: Tensor completion aims to recover a multi-dimensional array from its incomplete observations. The recently proposed tensor ring (TR) decomposition has powerful representation ability and shows promising performance in tensor completion, though they suffer from lack of theoretical guarantee. In this paper, we rigorously analyze the sample complexity of TR completion and find it also possesses the balance characteristic, which is consistent with the result of matrix completion. Inspired by this property we propose a nuclear norm minimization model and solve it by the alternating direction method of multipliers (ADMM). The experiments on synthetic data verify the theoretic analysis, and the numerical results of real-world data demonstrate that the proposed method gains great performance improvement in tensor completion compared with the state-of-the-art ones.

Includes members:
  Member "low rank tensor completion for multiway visual data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low rank tensor grid for image completion")
  Member "rank minimization on tensor ring a new paradigm in scalable tensor decomposition and completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the trouble with tensor ring decompositions")
  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 "learning efficient tensor representations with ring structure networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust tensor recovery using low rank tensor ring")
  Member "an iterative reweighted method for tucker decomposition of incomplete tensors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low rank tensor grid for image completion")
  Member "tensor completion by alternating minimization under the tensor train tt model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low rank tensor grid for image completion")
  Member "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "smooth parafac decomposition for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an efficient tensor completion method via new latent nuclear norm")

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)