human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a unified approach for learning the parameters of sum product networks"
  Abstract: We present a unified approach for learning the parameters of Sum-Product networks (SPNs). We prove that any complete and decomposable SPN is equivalent to a mixture of trees where each tree corresponds to a product of univariate distributions. Based on the mixture model perspective, we characterize the objective function when learning SPNs based on the maximum likelihood estimation (MLE) principle and show that the optimization problem can be formulated as a signomial program. We construct two parameter learning algorithms for SPNs by using sequential monomial approximations (SMA) and the concave-convex procedure (CCCP), respectively. The two proposed methods naturally admit multiplicative updates, hence effectively avoiding the projection operation. With the help of the unified framework, we also show that, in the case of SPNs, CCCP leads to the same algorithm as Expectation Maximization (EM) despite the fact that they are different in general.

Connected through hyperedge HE-1 (induced by source paper "on the sample complexity of learning sum product networks"):
  with members:
    - "bayesian learning of sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "sample efficient learning of mixtures"
    - "efficient robust proper learning of log concave distributions"
    - "expectation maximization for sum product networks as exponential family mixture models"
    - "on the expressive efficiency of sum product networks"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-2 (induced by source paper "bayesian learning of sum product networks"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "probabilistic deep learning using random sum product networks"
    - "tensorial mixture models"
    - "on the latent variable interpretation in sum product networks"
    - "neural architecture search with reinforcement learning"
    - "on the relationship between sum product networks and bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-3 (induced by source paper "deep generalized convolutional sum product networks for probabilistic image representations"):
  with members:
    - "tensorial mixture models"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-4 (induced by source paper "on the latent variable interpretation in sum product networks"):
  with members:
    - "the sum product theorem a foundation for learning tractable models"
    - "on the relationship between sum product networks and bayesian networks"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"
    - "map complexity results and approximation methods"

Connected through hyperedge HE-5 (induced by source paper "expectation maximization for sum product networks as exponential family mixture models"):
  with members:
    - "on the latent variable interpretation in sum product networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"

Question: Please tell me which class the center node 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)