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 "on the sample complexity of learning sum product networks"):
  Abstract: Sum-Product Networks (SPNs) can be regarded as a form of deep graphical models that compactly represent deeply factored and mixed distributions. An SPN is a rooted directed acyclic graph (DAG) consisting of a set of leaves (corresponding to base distributions), a set of sum nodes (which represent mixtures of their children distributions) and a set of product nodes (representing the products of its children distributions). #R##N#In this work, we initiate the study of the sample complexity of PAC-learning the set of distributions that correspond to SPNs. We show that the sample complexity of learning tree structured SPNs with the usual type of leaves (i.e., Gaussian or discrete) grows at most linearly (up to logarithmic factors) with the number of parameters of the SPN. More specifically, we show that the class of distributions that corresponds to tree structured Gaussian SPNs with $k$ mixing weights and $e$ ($d$-dimensional Gaussian) leaves can be learned within Total Variation error $\epsilon$ using at most $\widetilde{O}(\frac{ed^2+k}{\epsilon^2})$ samples. A similar result holds for tree structured SPNs with discrete leaves. #R##N#We obtain the upper bounds based on the recently proposed notion of distribution compression schemes. More specifically, we show that if a (base) class of distributions $\mathcal{F}$ admits an "efficient" compression, then the class of tree structured SPNs with leaves from $\mathcal{F}$ also admits an efficient compression.

Includes members:
  Member "bayesian learning of sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep structured mixtures of gaussian processes")
  Member "learning deep mixtures of gaussian process experts using sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conditional sum product networks imposing structure on deep probabilistic architectures")
  Member "tractable querying and learning in hybrid domains via sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "smt ilp")
  Member "sample efficient learning of mixtures":
    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 "efficient robust proper learning of log concave distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "expectation maximization for sum product networks as exponential family mixture models":
    also connected through other hyperedges:
      (none)
  Member "a unified approach for learning the parameters of sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian learning of sum product networks")
  Member "on the expressive efficiency of sum product networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep conditioning treatment of neural networks")

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)