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 "expectation maximization for sum product networks as exponential family mixture models"):
  Abstract: Sum-Product Networks (SPNs) are a recent class of probabilistic models which encode very large mixtures compactly by exploiting efficient reuse of computation in inference. Crucially, in SPNs the cost of inference scales linearly with the number of edges $E$ but the encoded mixture size $C$ can be exponentially larger than $E$. In this paper we obtain an efficient ($O(E)$) implementation of Expectation Maximization (EM) for SPNs which is the first to include EM updates both on mixture coefficients (corresponding to SPN weights) and mixture components (corresponding to SPN leaves). In particular, the update on mixture components translates to a weighted maximum likelihood problem on leaf distributions, and can be solved exactly when leaves are in the exponential family. This opens new application areas for SPNs, such as learning large mixtures of tree graphical models. We validate the algorithm on a synthetic but non trivial "soft-parity" distribution with $2^{n}$ modes encoded by a SPN with only $O(n)$ edges.

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 "a unified approach for learning the parameters of sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the sample complexity of learning sum product networks")
  Member "learning arithmetic circuits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning logistic circuits")
  Member "sum product networks a new deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)