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 "high dimension tensor completion via gradient based optimization under tensor train format"):
  Abstract: In this paper, we propose a novel approach to recover the missing entries of incomplete data represented by a high-dimension tensor. Tensor-train decomposition, which has powerful tensor representation ability and is free from `the curse of dimensionality', is employed in our approach. By observed entries of incomplete data, we consider to find the factors which can capture the latent features of the data and then reconstruct the missing entries. With low-rank assumption to the original data, tensor completion problem is cast into solving optimization models. Gradient descent methods are applied to optimize the core tensors of tensor-train decomposition. We propose two algorithms: Tensor-train Weighted Optimization (TT-WOPT) and Tensor-train Stochastic Gradient Descent (TT-SGD) to solve tensor completion problems. A high-order tensorization method named visual data tensorization (VDT) is proposed to transform visual data to higher-order forms by which the performance of our algorithms can be improved. The synthetic data experiments and visual data experiments show that our algorithms outperform the state-of-the-art completion algorithms. Especially in high-dimension, high missing rate and large-scale data cases, significant performance can be obtained from our algorithms.

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 "an efficient tensor completion method via new latent nuclear norm")
  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 "provable model for tensor ring completion")
  Member "efficient tensor completion low rank tensor train":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wide compression tensor ring nets")
  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 "tensor ring decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution")

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.NA(Numerical Analysis)