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 "tensor decompositions for learning latent variable models"):
  Abstract: This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation---which exploits a certain tensor structure in their low-order observable moments (typically, of second- and third-order). Specifically, parameter estimation is reduced to the problem of extracting a certain (orthogonal) decomposition of a symmetric tensor derived from the moments; this decomposition can be viewed as a natural generalization of the singular value decomposition for matrices. Although tensor decompositions are generally intractable to compute, the decomposition of these specially structured tensors can be efficiently obtained by a variety of approaches, including power iterations and maximization approaches (similar to the case of matrices). A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin's perturbation theorem for the singular vectors of matrices. This implies a robust and computationally tractable estimation approach for several popular latent variable models.

Includes members:
  Member "smoothed analysis of tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improper learning for non stochastic control")
  Member "the more the merrier the blessing of dimensionality for learning large gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "local loss optimization in operator models a new insight into spectral learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a maximum matching algorithm for basis selection in spectral learning")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable meta learning of linear representations")
  Member "a spectral algorithm for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "learning topic models going beyond svd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contrastive estimation reveals topic posterior information to linear models")
  Member "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture")

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)