human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "asymptotic model selection for directed networks with hidden variables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "inferring parameters and structure of latent variable models by variational bayes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational inference for sparse gaussian process modulated hawkes process")
  Member "learning the dimensionality of hidden variables":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning module networks")
  Member "spectral methods for learning multivariate latent tree structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guaranteed scalable learning of latent tree models")
  Member "learning high dimensional markov forest distributions analysis of error rates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "a spectral algorithm for learning hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch")
  Member "latent tree models and approximate inference in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent tree models for hierarchical topic detection")
  Member "learning nonsingular phylogenies and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "guaranteed scalable learning of latent tree models")

Question: Please tell me which class the source paper 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)