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 "latent tree analysis"):
  Abstract: Latent tree analysis seeks to model the correlations among a set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysis --- a method widely used in social sciences and medicine to identify homogeneous subgroups in a population. It provides new and fruitful perspectives on a number of machine learning areas, including cluster analysis, topic detection, and deep probabilistic modeling. This paper gives an overview of the research on latent tree analysis and various ways it is used in practice.

Includes members:
  Member "scalable latent tree model and its application to health analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods")
  Member "identification and classification of tcm syndrome types among patients with vascular mild cognitive impairment using latent tree analysis":
    also connected through other hyperedges:
      (none)
  Member "sparse boltzmann machines with structure learning as applied to text analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning sparse deep feedforward networks via tree skeleton expansion")
  Member "learning both weights and connections for efficient neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "progressive em for latent tree models and hierarchical topic detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conformative filtering for implicit feedback data")
  Member "a survey on latent tree models and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent factor analysis of gaussian distributions under graphical constraints")
  Member "latent tree models and approximate inference in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent tree models for hierarchical topic detection")

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)