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: "low rank tensor completion a riemannian manifold preconditioning approach"
  Abstract: We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint. A novel Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in Tucker decomposition. The specific metric allows to use the versatile framework of Riemannian optimization on quotient manifolds to develop preconditioned nonlinear conjugate gradient and stochastic gradient descent algorithms for batch and online setups, respectively. Concrete matrix representations of various optimization-related ingredients are listed. Numerical comparisons suggest that our proposed algorithms robustly outperform state-of-the-art algorithms across different synthetic and real-world datasets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "tensor p shrinkage nuclear norm for low rank tensor completion"):
  with members:
    - "low rank tensor completion for multiway visual data"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "smooth parafac decomposition for tensor completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "nuclear norm of higher order tensors"

Connected through hyperedge HE-3 (induced by source paper "tensor q rank a new data dependent tensor rank"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "parallel matrix factorization for low rank tensor completion"
    - "nuclear norm of higher order tensors"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "low rank tensor completion for multiway visual data"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "low rank tensor completion by truncated nuclear norm regularization"
    - "low tubal rank tensor completion using alternating minimization"
    - "scalable algorithms for tractable schatten quasi norm minimization"

Connected through hyperedge HE-5 (induced by source paper "attribute aware collaborative filtering survey and classification"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "on sampling strategies for neural network based collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "multi view machines"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "faster fast tensor completion with nonconvex regularization"):
  with members:
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "a dual framework for low rank tensor completion"
    - "low rank tensor recovery via iterative hard thresholding"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"

Connected through hyperedge HE-7 (induced by source paper "missing slice recovery for tensors using a low rank model in embedded space"):
  with members:
    - "smooth parafac decomposition for tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "inductive framework for multi aspect streaming tensor completion with side information"):
  with members:
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "relation schema induction using tensor factorization with side information"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "provable inductive matrix 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.LG(Machine Learning)