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: "efficient low rank tensor ring completion"
  Abstract: Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation. This development is motivated in part by the success of matrix completion algorithms that alternate over the (low-rank) factors. In this paper, we propose a spectral initialization for the tensor ring completion algorithm and analyze the computational complexity of the proposed algorithm. We numerically compare it with existing methods that employ a low rank tensor train approximation for data completion and show that our method outperforms the existing ones for a variety of real computer vision settings, and thus demonstrate the improved expressive power of tensor ring as compared to tensor train.

Connected through hyperedge HE-1 (induced by source paper "low rank tensor grid for image completion"):
  with members:
    - "provable model for tensor ring completion"
    - "low rank tensor completion for multiway visual data"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "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:
    - "provable model for tensor ring completion"
    - "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"
    - "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 "coupled tensor completion via low rank tensor ring"):
  with members:
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "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"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"

Connected through hyperedge HE-4 (induced by source paper "an efficient tensor completion method via new latent nuclear norm"):
  with members:
    - "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"
    - "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"
    - "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-5 (induced by source paper "manifold modeling in embedded space a perspective for interpreting deep image prior"):
  with members:
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "deep image prior"
    - "transportation analysis of denoising autoencoders a novel method for analyzing deep neural networks"
    - "smooth parafac decomposition for tensor completion"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "robust tensor recovery using low rank tensor ring"):
  with members:
    - "provable model for tensor ring completion"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "low rank tensor completion for multiway visual data"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "low rank tensor completion by truncated nuclear norm regularization"
    - "low tubal rank tensor completion using alternating minimization"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "reweighted low rank tensor completion and its applications in video recovery"

Connected through hyperedge HE-8 (induced by source paper "on deterministic sampling patterns for robust low rank matrix completion"):
  with members:
    - "fundamental conditions for low cp rank tensor completion"
    - "deterministic and probabilistic conditions for finite completability of low rank multi view data"
    - "rank determination for low rank data completion"
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor"
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "low tubal rank tensor completion using alternating minimization"
    - "fast algorithms for robust pca via gradient descent"
    - "nearly optimal robust matrix completion"

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)