human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"
  Abstract: In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from laborious model selection problem due to high model sensitivity. Especially for tensor ring (TR) decomposition, the number of model possibility grows exponentially with the tensor order, which makes it rather challenging to find the optimal TR decomposition. In this paper, by exploiting the low-rank structure on TR latent space, we propose a novel tensor completion method, which is robust to model selection. In contrast to imposing low-rank constraint on the data space, we introduce nuclear norm regularization on the latent TR factors, resulting in that the optimization step using singular value decomposition (SVD) can be performed at a much smaller scale. By leveraging the alternating direction method of multipliers (ADMM) scheme, the latent TR factors with optimal rank and the recovered tensor can be obtained simultaneously. Our proposed algorithm effectively alleviates the burden of TR-rank selection, therefore the computational cost is greatly reduced. The extensive experimental results on synthetic data and real-world data demonstrate the superior high performance and efficiency of the proposed approach against the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "provable tensor ring completion"):
  with members:
    - "provable model for tensor ring completion"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "the power of convex relaxation near optimal matrix completion"
    - "most tensor problems are np hard"

Connected through hyperedge HE-2 (induced by source paper "an efficient tensor completion method via new latent nuclear norm"):
  with members:
    - "higher dimension tensor completion via low rank tensor ring decomposition"
    - "high order tensor completion for data recovery via sparse tensor train optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"
    - "tensor methods and recommender systems"

Connected through hyperedge HE-3 (induced by source paper "tensor ring nuclear norm minimization and application for visual data completion"):
  with members:
    - "higher dimension tensor completion via low rank tensor ring decomposition"
    - "exact tensor completion using t svd"

Connected through hyperedge HE-4 (induced by source paper "high dimension tensor completion via gradient based optimization under tensor train format"):
  with members:
    - "higher dimension tensor completion via low rank tensor ring decomposition"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "learning efficient tensor representations with ring structure networks"
    - "efficient tensor completion low rank tensor train"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor ring decomposition"
    - "an overview of gradient descent optimization algorithms"

Question: Please tell me which class the center node 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)