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: "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"
  Abstract: Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches---such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)---amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g. missing data and binary data), and (iii) noisy observations and outliers. To address these issues, we propose tensor-variate latent nonparametric Bayesian models, coupled with efficient inference methods, for multiway data analysis. We name these models InfTucker. Using these InfTucker, we conduct Tucker decomposition in an infinite feature space. Unlike classical tensor decomposition models, our new approaches handle both continuous and binary data in a probabilistic framework. Unlike previous Bayesian models on matrices and tensors, our models are based on latent Gaussian or $t$ processes with nonlinear covariance functions. To efficiently learn the InfTucker from data, we develop a variational inference technique on tensors. Compared with classical implementation, the new technique reduces both time and space complexities by several orders of magnitude. Our experimental results on chemometrics and social network datasets demonstrate that our new models achieved significantly higher prediction accuracy than the most state-of-art tensor decomposition

Connected through hyperedge HE-1 (induced by source paper "learning compact recurrent neural networks with block term tensor decomposition"):
  with members:
    - "tensor regression networks"
    - "tensor train recurrent neural networks for video classification"
    - "compressing recurrent neural network with tensor train"
    - "tensorial recurrent neural networks for longitudinal data analysis"
    - "sharing residual units through collective tensor factorization in deep neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "learning efficient tensor representations with ring structure networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "vectorial dimension reduction for tensors based on bayesian inference"):
  with members:
    - "tensorizing neural networks"
    - "dictionary learning and sparse coding for third order super symmetric tensors"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "a bayesian tensor factorization model via variational inference for link prediction"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (induced by source paper "beyond low rank a data adaptive tensor completion method"):
  with members:
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-5 (induced by source paper "linked component analysis from matrices to high order tensors applications to biomedical data"):
  with members:
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "tensor ring decomposition"):
  with members:
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "efficient tensor completion low rank tensor train"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "tensorizing neural networks"
    - "matrix product state for feature extraction of higher order tensors"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"

Connected through hyperedge HE-7 (induced by source paper "bayesian robust tensor factorization for incomplete multiway data"):
  with members:
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "tensor rank some lower and upper bounds"
    - "robust pca via outlier pursuit"
    - "most tensor problems are np hard"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "bayesian cp factorization of incomplete tensors with automatic rank determination"):
  with members:
    - "large margin low rank tensor analysis"
    - "tensor rank some lower and upper 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.LG(Machine Learning)