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 "seeding k means using method of moments"):
  Abstract: K-means is one of the most widely used algorithms for clustering in Data Mining applications, which attempts to minimize the sum of the square of the Euclidean distance of the points in the clusters from the respective means of the clusters. However, K-means suffers from local minima problem and is not guaranteed to converge to the optimal cost. K-means++ tries to address the problem by seeding the means using a distance-based sampling scheme. However, seeding the means in K-means++ needs $O\left(K\right)$ sequential passes through the entire dataset, and this can be very costly for large datasets. Here we propose a method of seeding the initial means based on factorizations of higher order moments for bounded data. Our method takes $O\left(1\right)$ passes through the entire dataset to extract the initial set of means, and its final cost can be proven to be within $O(\sqrt{K})$ of the optimal cost. We demonstrate the performance of our algorithm in comparison with the existing algorithms on various benchmark datasets.

Includes members:
  Member "spectral experts for estimating mixtures of linear regressions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning for mixed linear regression")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "learning mixtures of spherical gaussians moment methods and spectral decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians")
  Member "a spectral algorithm for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "scalable k means":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "revisiting k means new algorithms via bayesian nonparametrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "infinite mixture prototypes for few shot learning")
  Member "a spectral algorithm for learning hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch")

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)