human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "variational inference for monte carlo objectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "importance weighted autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "muprop unbiased backpropagation for stochastic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "neural adaptive sequential monte carlo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "bidirectional helmholtz machines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introduction to variational autoencoders")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "reweighted wake sleep":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta amortized variational inference and learning")
  Member "neural variational inference and learning in belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")

Question: Please tell me which class the source paper 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)