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: "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.

Connected through hyperedge HE-1 (induced by source paper "low rank tensor grid for image completion"):
  with members:
    - "low rank tensor completion for multiway visual data"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "an iterative reweighted method for tucker decomposition of incomplete tensors"
    - "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"
    - "parallel matrix factorization for low rank tensor completion"

Connected through hyperedge HE-2 (induced by source paper "provable tensor ring completion"):
  with members:
    - "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor 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-3 (induced by source paper "robust tensor recovery using low rank tensor ring"):
  with members:
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "learning efficient tensor representations with ring structure networks"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "a new convex relaxation for tensor completion"
    - "most tensor problems are np hard"
    - "robust principal component analysis"

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)