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: "spectral methods for learning multivariate latent tree structure"
  Abstract: This work considers the problem of learning the structure of multivariate linear tree models, which include a variety of directed tree graphical models with continuous, discrete, and mixed latent variables such as linear-Gaussian models, hidden Markov models, Gaussian mixture models, and Markov evolutionary trees. The setting is one where we only have samples from certain observed variables in the tree, and our goal is to estimate the tree structure (i.e., the graph of how the underlying hidden variables are connected to each other and to the observed variables). We propose the Spectral Recursive Grouping algorithm, an efficient and simple bottom-up procedure for recovering the tree structure from independent samples of the observed variables. Our finite sample size bounds for exact recovery of the tree structure reveal certain natural dependencies on underlying statistical and structural properties of the underlying joint distribution. Furthermore, our sample complexity guarantees have no explicit dependence on the dimensionality of the observed variables, making the algorithm applicable to many high-dimensional settings. At the heart of our algorithm is a spectral quartet test for determining the relative topology of a quartet of variables from second-order statistics.

Connected through hyperedge HE-1 (induced by source paper "guaranteed scalable learning of latent tree models"):
  with members:
    - "beyond physical connections tree models in human pose estimation"
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "tensor decompositions for learning latent variable models"
    - "efficient active algorithms for hierarchical clustering"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "scalable latent tree model and its application to health analytics"):
  with members:
    - "beyond physical connections tree models in human pose estimation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "tensor decompositions for learning latent variable models"
    - "efficient active algorithms for hierarchical clustering"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "spectral methods for nonparametric models"):
  with members:
    - "a tensor approach to learning mixed membership community models"
    - "hilbert space embeddings of predictive state representations"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "progressive em for latent tree models and hierarchical topic detection"):
  with members:
    - "discovering structure in high dimensional data through correlation explanation"

Connected through hyperedge HE-6 (induced by source paper "discovering structure in high dimensional data through correlation explanation"):
  with members:
    - "exact common information"
    - "quantifying synergistic mutual information"
    - "intriguing properties of neural networks"
    - "efficient estimation of word representations in vector space"
    - "a survey on latent tree models and applications"
    - "representation learning a review and new perspectives"
    - "nonnegative decomposition of multivariate information"
    - "information theoretic inference of common ancestors"

Connected through hyperedge HE-7 (induced by source paper "a survey on latent tree models and applications"):
  with members:
    - "asymptotic model selection for directed networks with hidden variables"
    - "inferring parameters and structure of latent variable models by variational bayes"
    - "learning the dimensionality of hidden variables"
    - "learning high dimensional markov forest distributions analysis of error rates"
    - "a spectral algorithm for learning hidden markov models"
    - "latent tree models and approximate inference in bayesian networks"
    - "learning nonsingular phylogenies and hidden markov models"
    - "effective dimensions of hierarchical latent class models"

Connected through hyperedge HE-8 (induced by source paper "unfolding latent tree structures using 4th order tensors"):
  with members:
    - "why neighbor joining works"

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)