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: "probabilistic deep learning using random sum product networks"
  Abstract: The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods. However, most current approaches have severe limitations when it comes to inference, since many of these models do not even permit to evaluate exact data likelihoods. Sum-product networks (SPNs), on the other hand, are an excellent architecture in that regard, as they allow to efficiently evaluate likelihoods, as well as arbitrary marginalization and conditioning tasks. Nevertheless, SPNs have not been fully explored as serious deep learning models, likely due to their special structural requirements, which complicate learning. In this paper, we make a drastic simplification and use random SPN structures which are trained in a "classical deep learning manner", i.e. employing automatic differentiation, SGD, and GPU support. The resulting models, called RAT-SPNs, yield prediction results comparable to deep neural networks, while still being interpretable as generative model and maintaining well-calibrated uncertainties. This property makes them highly robust under missing input features and enables them to naturally detect outliers and peculiar samples.

Connected through hyperedge HE-1 (induced by source paper "conditional sum product networks imposing structure on deep probabilistic architectures"):
  with members:
    - "visualizing and understanding sum product networks"
    - "learning logistic circuits"
    - "on the relative expressiveness of bayesian and neural networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "on relaxing determinism in arithmetic circuits"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "sum product quotient networks"
    - "on the latent variable interpretation in sum product networks"

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"
    - "tensorial mixture models"
    - "on the latent variable interpretation in sum product networks"
    - "a unified approach for learning the parameters of 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 convolutional sum product networks for probabilistic image representations"):
  with members:
    - "on the convergence of adam and beyond"
    - "tensorial mixture models"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "optimisation of overparametrized sum product networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "learning deep generative spatial models for mobile robots"
    - "on the latent variable interpretation in sum product networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-5 (induced by source paper "sum product network decompilation"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "on the relationship between sum product networks and bayesian networks"
    - "context specific independence in bayesian networks"
    - "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)