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 tensor completion low rank tensor train"
  Abstract: This paper proposes a novel formulation of the tensor completion problem to impute missing entries of data represented by tensors. The formulation is introduced in terms of tensor train (TT) rank which can effectively capture global information of tensors thanks to its construction by a well-balanced matricization scheme. Two algorithms are proposed to solve the corresponding tensor completion problem. The first one called simple low-rank tensor completion via tensor train (SiLRTC-TT) is intimately related to minimizing the TT nuclear norm. The second one is based on a multilinear matrix factorization model to approximate the TT rank of the tensor and called tensor completion by parallel matrix factorization via tensor train (TMac-TT). These algorithms are applied to complete both synthetic and real world data tensors. Simulation results of synthetic data show that the proposed algorithms are efficient in estimating missing entries for tensors with either low Tucker rank or TT rank while Tucker-based algorithms are only comparable in the case of low Tucker rank tensors. When applied to recover color images represented by ninth-order tensors augmented from third-order ones, the proposed algorithms outperforms the Tucker-based algorithms.

Connected through hyperedge HE-1 (induced by source paper "wide compression tensor ring nets"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "tensor train neighborhood preserving embedding"
    - "topic compositional neural language model"
    - "tensor train recurrent neural networks for video classification"
    - "rank determination for low rank data completion"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "fast and accurate tensor completion with tensor trains a system identification approach"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "tensor network alternating linear scheme for mimo volterra system identification"
    - "parallel matrix factorization for low rank tensor completion"
    - "incoherence optimal matrix completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "efficient low rank tensor ring completion"):
  with members:
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "tensor ring decomposition"
    - "tensorizing neural networks"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "tensor networks for big data analytics and large scale optimization problems"

Connected through hyperedge HE-5 (induced by source paper "tensor ring decomposition"):
  with members:
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "tensorizing neural networks"
    - "matrix product state for feature extraction of higher order tensors"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"

Connected through hyperedge HE-6 (induced by source paper "tensor completion by alternating minimization under the tensor train tt model"):
  with members:
    - "novel factorization strategies for higher order tensors implications for compression and recovery of multi linear data"

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)