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 sum product network leaves with expectation maximization"):
  Abstract: It has been shown in recent work that Expectation-Maximization (EM) is an attractive way to learn the parameters of Sum-Product Networks (SPNs). However, previous derivations of EM do not easily extend to SPNs with general leaf distributions and shared parameters. In this paper we obtain a new derivation of EM for SPNs allowing to derive EM updates for \emph{arbitrary leaf distributions} and for \emph{shared parameters} between SPN nodes. Here, EM translates in a set of independent weighted maximum likelihood problems for each leaf distribution, and therefore \emph{any} distribution that can be trained with maximum likelihood (even approximately) fits in this framework. The ability of learning complex leaf distributions in a principled way makes SPN parameter learning more flexible than learning SPN weights only. We demonstrate the potential of this approach with extensive experiments on twenty widely used benchmark datasets, using tree graphical models as leaf distributions.

Includes members:
  Member "on the latent variable interpretation in sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advances in machine learning for the behavioral sciences")
  Member "learning arithmetic circuits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning logistic circuits")
  Member "sum product networks a new deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training deep energy based models with f divergence minimization")

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)