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: "using large ensembles of control variates for variational inference"
  Abstract: Variational inference is increasingly being addressed with stochastic optimization. In this setting, the gradient's variance plays a crucial role in the optimization procedure, since high variance gradients lead to poor convergence. A popular approach used to reduce gradient's variance involves the use of control variates. Despite the good results obtained, control variates developed for variational inference are typically looked at in isolation. In this paper we clarify the large number of control variates that are available by giving a systematic view of how they are derived. We also present a Bayesian risk minimization framework in which the quality of a procedure for combining control variates is quantified by its effect on optimization convergence rates, which leads to a very simple combination rule. Results show that combining a large number of control variates this way significantly improves the convergence of inference over using the typical gradient estimators or a reduced number of control variates.

Connected through hyperedge HE-1 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "credit assignment techniques in stochastic computation graphs"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "new tricks for estimating gradients of expectations"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "clipped action policy gradient"
    - "understanding and correcting pathologies in the training of learned optimizers"

Connected through hyperedge HE-2 (induced by source paper "a rule for gradient estimator selection with an application to variational inference"):
  with members:
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"
    - "neural variational inference and learning in belief networks"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning when all actions are not always available"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "planning and learning with stochastic action sets"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"
    - "online advertisement optimization and stochastic networks"

Connected through hyperedge HE-4 (induced by source paper "divide and couple using monte carlo variational objectives for posterior approximation"):
  with members:
    - "importance weighting and variational inference"
    - "filtering variational objectives"
    - "fast black box variational inference through stochastic trust region optimization"
    - "importance weighted autoencoders"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-5 (induced by source paper "new tricks for estimating gradients of expectations"):
  with members:
    - "dice the infinitely differentiable monte carlo estimator"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "muprop unbiased backpropagation for stochastic neural networks"

Connected through hyperedge HE-6 (induced by source paper "provable gradient variance guarantees for black box variational inference"):
  with members:
    - "provable smoothness guarantees for black box variational inference"
    - "fast black box variational inference through stochastic trust region optimization"
    - "learning an astronomical catalog of the visible universe through scalable bayesian inference"

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)