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 for color image and video recovery low rank tensor train"
  Abstract: This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.

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 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:
    - "provable model for tensor ring completion"
    - "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"
    - "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 "coupled tensor completion via low rank tensor ring"):
  with members:
    - "efficient low rank tensor ring completion"
    - "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 "lossless compression for 3dcnns based on tensor train decomposition"):
  with members:
    - "tensor train decomposition on tensorflow t3f"
    - "tensor decomposition for compressing recurrent neural network"
    - "text independent speaker verification using 3d convolutional neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "expressive power of recurrent neural networks"
    - "deformable convolutional networks"
    - "tensor train recurrent neural networks for video classification"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-5 (induced by source paper "provable model for tensor ring completion"):
  with members:
    - "low rank tensor completion for multiway visual data"
    - "rank minimization on tensor ring a new paradigm in scalable tensor decomposition and completion"
    - "learning efficient tensor representations with ring structure networks"
    - "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"
    - "smooth parafac decomposition for tensor completion"
    - "parallel matrix factorization for low rank tensor completion"

Connected through hyperedge HE-6 (induced by source paper "robust tensor completion using transformed tensor svd"):
  with members:
    - "exact tensor completion from sparsely corrupted observations via convex optimization"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "parallel matrix factorization for low rank tensor completion"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "spectral unmixing via data guided sparsity"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-7 (induced by source paper "color image and multispectral image denoising using block diagonal representation"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "renoir a dataset for real low light image noise reduction"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

Connected through hyperedge HE-8 (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 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"

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)