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: "parallel matrix factorization for low rank tensor completion"
  Abstract: Higher-order low-rank tensors naturally arise in many applications including hyperspectral data recovery, video inpainting, seismic data reconstruction, and so on. #R##N#We propose a new model to recover a low-rank tensor by simultaneously performing low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An alternating minimization algorithm is applied to solve the model, along with two adaptive rank-adjusting strategies when the exact rank is not known. #R##N#   &nbsp#R##N#Phase transition plots reveal that  our algorithm can recover    a variety of synthetic low-rank tensors  from significantly fewer samples than the compared methods, which include a matrix completion method applied to tensor recovery and two state-of-the-art tensor completion methods.  Further tests on real-world data show similar advantages. Although our model is non-convex, our algorithm performs consistently throughout the tests and gives better results than the compared methods, some of which are based on convex models. In addition, subsequence convergence of our algorithm can be established in the sense that any limit point of the iterates satisfies the KKT condtions.

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"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "generalized singular value thresholding"

Connected through hyperedge HE-3 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "fundamental conditions for low cp rank tensor completion"
    - "exact tensor completion with sum of squares"
    - "on the optimization landscape of tensor decompositions"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-4 (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-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"
    - "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-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"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "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 "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"

Connected through hyperedge HE-8 (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"
    - "efficient low rank tensor ring completion"
    - "transportation analysis of denoising autoencoders a novel method for analyzing deep neural networks"
    - "smooth parafac decomposition for tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "adam a method for stochastic optimization"

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)