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: "rank regularization and bayesian inference for tensor completion and extrapolation"
  Abstract: A novel regularizer of the PARAFAC decomposition factors capturing the tensor's rank is proposed in this paper, as the key enabler for completion of three-way data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and prediction capabilities. This probabilistic approach can naturally accommodate general models for the data distribution, lending itself to various fitting criteria that yield optimum estimates in the maximum-a-posteriori sense. In particular, two algorithms are devised for Gaussian- and Poisson-distributed data, that minimize the rank-regularized least-squares error and Kullback-Leibler divergence, respectively. The proposed technique is able to recover the “ground-truth” tensor rank when tested on synthetic data, and to complete brain imaging and yeast gene expression datasets with 50% and 15% of missing entries respectively, resulting in recovery errors at -11 dB and -15 dB.

Connected through hyperedge HE-1 (induced by source paper "coupled tensor completion via low rank tensor ring"):
  with members:
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "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"

Connected through hyperedge HE-2 (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-3 (induced by source paper "bayesian tensorized neural networks with automatic rank selection"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "wide compression tensor ring nets"
    - "tensor regression networks"
    - "compression aware training of deep networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "long term forecasting using higher order tensor rnns"
    - "ultimate tensorization compressing convolutional and fc layers alike"
    - "bayesian robust tensor factorization for incomplete multiway data"

Connected through hyperedge HE-4 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "a tensor approach to learning mixed membership community models"
    - "online tensor methods for learning latent variable models"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "community detection in networks with node attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "near optimal sample complexity for convex tensor completion"):
  with members:
    - "matrix completion via max norm constrained optimization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "exact tensor completion using t svd"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "nuclear norm of higher order tensors"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "concentration based guarantees for low rank matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "fast online low rank tensor subspace tracking by cp decomposition using recursive least squares from incomplete observations"):
  with members:
    - "inductive framework for multi aspect streaming tensor completion with side information"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "res regularized stochastic bfgs algorithm"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"

Connected through hyperedge HE-7 (induced by source paper "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "a comparison of mimo techniques in downlink millimeter wave cellular networks with hybrid beamforming"
    - "delay and backlog analysis for 60 ghz wireless networks"
    - "achievable rate of rician large scale mimo channels with transceiver hardware impairments"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Connected through hyperedge HE-8 (induced by source paper "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "spatially sparse precoding in millimeter wave mimo 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.IT(Information Theory)