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 "learning arbitrary statistical mixtures of discrete distributions"):
  Abstract: We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$ directly, but only indirectly and in very noisy fashion, by sampling from $[n]$ repeatedly, independently $K$ times from the distribution $p$. The problem is to infer $\vartheta$ to high accuracy in transportation (earthmover) distance. #R##N#We give the first efficient algorithms for learning this mixture model without making any restricting assumptions on the structure of the distribution $\vartheta$. We bound the quality of the solution as a function of the size of the samples $K$ and the number of samples used. Our model and results have applications to a variety of unsupervised learning scenarios, including learning topic models and collaborative filtering.

Includes members:
  Member "probabilistic latent semantic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation")
  Member "learning mixtures of arbitrary distributions over large discrete domains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning sums of independent random variables with sparse collective support")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "contrastive estimation reveals topic posterior information to linear models")
  Member "learning k modal distributions via testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "settling the polynomial learnability of mixtures of gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "polynomial learning of distribution families":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private hypothesis selection")

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)