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: "iterative refinement of approximate posterior for training directed belief networks"
  Abstract: Recent advances in variational inference that make use of an inference or recognition network for training and evaluating deep directed graphical models have advanced well beyond traditional variational inference and Markov chain Monte Carlo methods. These techniques offer higher flexibility with simpler and faster inference; yet training and evaluation still remains a challenge. We propose a method for improving the per-example approximate posterior by iterative refinement, which can provide notable gains in maximizing the variational lower bound of the log likelihood and works with both continuous and discrete latent variables. We evaluate our approach as a method of training and evaluating directed graphical models. We show that, when used for training, iterative refinement improves the variational lower bound and can also improve the log-likelihood over related methods. We also show that iterative refinement can be used to get a better estimate of the log-likelihood in any directed model trained with mean-field inference.

Connected through hyperedge HE-1 (induced by source paper "inference suboptimality in variational autoencoders"):
  with members:
    - "the reversible residual network backpropagation without storing activations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"
    - "measuring the reliability of mcmc inference with bidirectional monte carlo"
    - "on the quantitative analysis of decoder based generative models"
    - "importance weighted autoencoders"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-2 (induced by source paper "variational autoencoders for feature detection of magnetic resonance imaging data"):
  with members:
    - "nice non linear independent components estimation"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "deep learning for neuroimaging a validation study"

Connected through hyperedge HE-3 (induced by source paper "bidirectional helmholtz machines"):
  with members:
    - "importance weighted autoencoders"
    - "blocks and fuel frameworks for deep learning"
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "adam a method for stochastic optimization"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "accurate and conservative estimates of mrf log likelihood using reverse annealing"
    - "unit tests for stochastic optimization"

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)