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: "reweighted wake sleep"
  Abstract: Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoidal belief network is not sufficiently powerful for the layers of the inference network in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.

Connected through hyperedge HE-1 (induced by source paper "meta amortized variational inference and learning"):
  with members:
    - "attentive neural processes"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "variational memory addressing in generative models"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "imitation learning of factored multi agent reactive models"):
  with members:
    - "generating multi agent trajectories using programmatic weak supervision"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "fixing a broken elbo"
    - "generative models of visually grounded imagination"
    - "generative temporal models with memory"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-3 (induced by source paper "the thermodynamic variational objective"):
  with members:
    - "implicit reparameterization gradients"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "filtering variational objectives"
    - "inference compilation and universal probabilistic programming"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-4 (induced by source paper "hierarchical importance weighted autoencoders"):
  with members:
    - "importance weighting and variational inference"
    - "neural importance sampling"
    - "differentiable antithetic sampling for variance reduction in stochastic variational inference"
    - "inference suboptimality in variational autoencoders"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "filtering variational objectives"
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"

Connected through hyperedge HE-5 (induced by source paper "a review of learning with deep generative models from perspective of graphical modeling"):
  with members:
    - "cgans with projection discriminator"
    - "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "inference suboptimality in variational autoencoders"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "advances in variational inference"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "amr parsing as graph prediction with latent alignment"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "text summarization using abstract meaning representation"
    - "addressing the data sparsity issue in neural amr parsing"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "a structured variational autoencoder for contextual morphological inflection"):
  with members:
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "enriching word vectors with subword information"
    - "effective approaches to attention based neural machine translation"
    - "morphological inflection generation using character sequence to sequence learning"
    - "conditional random field autoencoders for unsupervised structured prediction"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-8 (induced by source paper "on the information theoretic distance measures and bidirectional helmholtz machines"):
  with members:
    - "importance weighted autoencoders"

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)