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: "fast black box variational inference through stochastic trust region optimization"
  Abstract: We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, TrustVI proposes and assesses a step based on minibatches of draws from the variational distribution. The algorithm provably converges to a stationary point. We implemented TrustVI in the Stan framework and compared it to two alternatives: Automatic Differentiation Variational Inference (ADVI) and Hessian-free Stochastic Gradient Variational Inference (HFSGVI). The former is based on stochastic first-order optimization. The latter uses second-order information, but lacks convergence guarantees. TrustVI typically converged at least one order of magnitude faster than ADVI, demonstrating the value of stochastic second-order information. TrustVI often found substantially better variational distributions than HFSGVI, demonstrating that our convergence theory can matter in practice.

Connected through hyperedge HE-1 (induced by source paper "divide and couple using monte carlo variational objectives for posterior approximation"):
  with members:
    - "importance weighting and variational inference"
    - "using large ensembles of control variates for variational inference"
    - "filtering variational objectives"
    - "importance weighted autoencoders"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-2 (induced by source paper "the variational predictive natural gradient"):
  with members:
    - "generating sentences from a continuous space"
    - "neural variational inference for text processing"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-3 (induced by source paper "provable smoothness guarantees for black box variational inference"):
  with members:
    - "conjugate computation variational inference converting variational inference in non conjugate models to inferences in conjugate models"
    - "boosting variational inference an optimization perspective"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "provable gradient variance guarantees for black box variational inference"):
  with members:
    - "provable smoothness guarantees for black box variational inference"
    - "using large ensembles of control variates for variational inference"
    - "learning an astronomical catalog of the visible universe through scalable bayesian inference"

Connected through hyperedge HE-5 (induced by source paper "trust region variational inference with gaussian mixture models"):
  with members:
    - "model free trajectory based policy optimization with monotonic improvement"
    - "trust region policy optimization"
    - "nonparametric variational inference"

Connected through hyperedge HE-6 (induced by source paper "stochastic cubic regularization for fast nonconvex optimization"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "a generic approach for escaping saddle points"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "a geometric analysis of phase retrieval"
    - "tensorflow a system for large scale machine learning"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"

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)