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: "distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server"
  Abstract: This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks. #R##N#Keywords: Distributed Learning, Large Scale Learning, Deep Learning, Bayesian Learn- ing, Variational Inference, Expectation Propagation, Stochastic Approximation, Natural Gradient, Markov chain Monte Carlo, Parameter Server, Posterior Server.

Connected through hyperedge HE-1 (induced by source paper "controlling model complexity in probabilistic model based dynamic optimization of neural network structures"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "diagonal acceleration for covariance matrix adaptation evolution strategies"):
  with members:
    - "lm cma an alternative to l bfgs for large scale black box optimization"
    - "efficient natural evolution strategies"
    - "alternative restart strategies for cma es"

Connected through hyperedge HE-3 (induced by source paper "bayesian recurrent neural networks"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning to learn by gradient descent by gradient descent"
    - "stochastic backpropagation through mixture density distributions"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "learning to optimize"
    - "recurrent highway networks"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"

Connected through hyperedge HE-4 (induced by source paper "deep exploration via bootstrapped dqn"):
  with members:
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "end to end training of deep visuomotor policies"
    - "learning to optimize via posterior sampling"
    - "the arcade learning environment an evaluation platform for general agents"

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)