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: "a survey on latent tree models and applications"
  Abstract: In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field.

Connected through hyperedge HE-1 (induced by source paper "latent factor analysis of gaussian distributions under graphical constraints"):
  with members:
    - "model approximation using cascade of tree decompositions"
    - "learning latent superstructures in variational autoencoders for deep multidimensional clustering"

Connected through hyperedge HE-2 (induced by source paper "tensorial mixture models"):
  with members:
    - "improving variational inference with inverse autoregressive flow"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "semi supervised learning with deep generative models"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "sum product quotient networks"):
  with members:
    - "on the expressive power of overlapping architectures of deep learning"
    - "neural networks and rational functions"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-4 (induced by source paper "successive synthesis of latent gaussian trees"):
  with members:
    - "synthesis of gaussian trees with correlation sign ambiguity an information theoretic approach"
    - "chernoff information of bottleneck gaussian trees"
    - "gaussian secure source coding and wyner s common information"
    - "distributed channel synthesis"

Connected through hyperedge HE-5 (induced by source paper "latent tree models for hierarchical topic detection"):
  with members:
    - "topic browsing for research papers with hierarchical latent tree analysis"
    - "sparse boltzmann machines with structure learning as applied to text analysis"
    - "discovering structure in high dimensional data through correlation explanation"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a method of moments for mixture models and hidden markov models"
    - "latent tree models and approximate inference in bayesian networks"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-6 (induced by source paper "latent tree analysis"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "identification and classification of tcm syndrome types among patients with vascular mild cognitive impairment using latent tree analysis"
    - "sparse boltzmann machines with structure learning as applied to text analysis"
    - "learning both weights and connections for efficient neural networks"
    - "progressive em for latent tree models and hierarchical topic detection"
    - "latent tree models and approximate inference in bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "layered synthesis of latent gaussian trees"):
  with members:
    - "synthesis of gaussian trees with correlation sign ambiguity an information theoretic approach"
    - "distributed simulation of continuous random variables"
    - "chernoff information of bottleneck gaussian trees"
    - "gaussian secure source coding and wyner s common information"

Connected through hyperedge HE-8 (induced by source paper "towards building deep networks with bayesian factor graphs"):
  with members:
    - "deep learning in neural networks"
    - "representation learning a review and new perspectives"

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)