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: "bayesian robust tensor factorization for incomplete multiway data"
  Abstract: We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CANDECOMP/PARAFAC (CP)-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-t distribution that associates an individual hyperparameter with each element independently. For model learning, we develop an efficient variational inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without the need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world data sets demonstrate the superiorities of our method from several perspectives.

Connected through hyperedge HE-1 (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"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (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"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"
    - "tensor methods and recommender systems"

Connected through hyperedge HE-3 (induced by source paper "active collaborative sensing for energy breakdown"):
  with members:
    - "a survey on non intrusive load monitoring methodies and techniques for energy disaggregation problem"
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "how good is good enough re evaluating the bar for energy disaggregation"
    - "nilmtk v0 2 a non intrusive load monitoring toolkit for large scale data sets demo abstract"
    - "a contextual bandit approach to personalized news article recommendation"
    - "active learning with statistical models"

Connected through hyperedge HE-4 (induced by source paper "tensor ring decomposition with rank minimization on latent space an efficient approach for tensor completion"):
  with members:
    - "wide compression tensor ring nets"
    - "higher dimension tensor completion via low rank tensor ring decomposition"
    - "tensor ring decomposition"
    - "tensorizing neural networks"
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-5 (induced by source paper "tensor networks for latent variable analysis higher order canonical polyadic decomposition"):
  with members:
    - "vectorial dimension reduction for tensors based on bayesian inference"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "speeding up convolutional neural networks with low rank expansions"
    - "smoothed analysis of tensor decompositions"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

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 cp factorization of incomplete tensors with automatic rank determination"
    - "res regularized stochastic bfgs algorithm"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"

Connected through hyperedge HE-7 (induced by source paper "beyond low rank a data adaptive tensor completion method"):
  with members:
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "encoding high dimensional local features by sparse coding based fisher vectors"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"

Connected through hyperedge HE-8 (induced by source paper "linked component analysis from matrices to high order tensors applications to biomedical data"):
  with members:
    - "smooth parafac decomposition for tensor completion"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "robust principal component analysis on graphs"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "tensor networks for big data analytics and large scale optimization problems"
    - "independent vector analysis identification conditions and performance bounds"

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)