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: "novel methods for multilinear data completion and de noising based on tensor svd"
  Abstract: In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data. We first show that videos with linear camera motion can be represented more efficiently using t-SVD compared to the approaches based on vectorizing or flattening of the tensors. Since efficiency in representation implies efficiency in recovery, we outline a tensor nuclear norm penalized algorithm for video completion from missing entries. Application of the proposed algorithm for video recovery from missing entries is shown to yield a superior performance over existing methods. We also consider the problem of tensor robust Principal Component Analysis (PCA) for de-noising 3-D video data from sparse random corruptions. We show superior performance of our method compared to the matrix robust PCA adapted to this setting as proposed in [4].

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "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"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"
    - "generalized singular value thresholding"

Connected through hyperedge HE-3 (induced by source paper "generalized visual information analysis via tensorial algebra"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "the power and arnoldi methods in an algebra of circulants"

Connected through hyperedge HE-4 (induced by source paper "tensor graph convolutional networks for prediction on dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "stable tensor neural networks for rapid deep learning"
    - "community interaction and conflict on the web"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-5 (induced by source paper "deep plug and play prior for low rank tensor completion"):
  with members:
    - "plug and play methods provably converge with properly trained denoisers"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "low rank tensor completion for multiway visual data"
    - "regularization by denoising clarifications and new interpretations"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "image restoration by iterative denoising and backward projections"

Connected through hyperedge HE-6 (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"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "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"

Connected through hyperedge HE-7 (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"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "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"

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 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"

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.CV(Computer Vision and Pattern Recognition)