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: "higher dimension tensor completion via low rank tensor ring decomposition"
  Abstract: The problem of incomplete data is common in signal processing and machine learning. Tensor completion algorithms aim to recover the incomplete data from its partially observed entries. In this paper, taking advantages of high compressibility and flexibility of recently proposed tensor ring (TR) decomposition, we propose a new tensor completion approach named tensor ring weighted optimization (TR-WOPT). It finds the latent factors of the incomplete tensor by gradient descent algorithm, then the latent factors are employed to predict the missing entries of the tensor. We conduct various tensor completion experiments on synthetic data and real-world data. The simulation results show that TR-WOPT performs well in various high-dimension tensors. Furthermore, image completion results show that our proposed algorithm outperforms the state-of-the-art algorithms in many situations. Especially when the missing rate of the test images is high (e.g., over 0.9), the performance of our TR-WOPT is significantly better than the compared algorithms.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "tensor grid decomposition with application to tensor completion"):
  with members:
    - "exact recovery of low rank tensor decomposition under reshuffling"
    - "learning efficient tensor representations with ring structure networks"
    - "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 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"
    - "most tensor problems are np hard"

Connected through hyperedge HE-3 (induced by source paper "tensor ring nuclear norm minimization and application for visual data completion"):
  with members:
    - "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"
    - "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:
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"):
  with members:
    - "wide compression tensor ring nets"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "tensor ring decomposition"
    - "tensorizing neural networks"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor decompositions for learning latent variable models"

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.NA(Numerical Analysis)