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: "importance weighting and variational inference"
  Abstract: Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying the looseness in previous work. Experiments confirm IWVI's practicality for probabilistic inference. As a second contribution, we investigate inference with elliptical distributions, which improves accuracy in low dimensions, and convergence in high dimensions.

Connected through hyperedge HE-1 (induced by source paper "learning discrete distributions by dequantization"):
  with members:
    - "integer discrete flows and lossless compression"
    - "discrete flows invertible generative models of discrete data"
    - "mintnet building invertible neural networks with masked convolutions"
    - "emerging convolutions for generative normalizing flows"
    - "learning likelihoods with conditional normalizing flows"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "kernel exponential family estimation via doubly dual embedding"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "hamiltonian variational auto encoder"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "stochastic video generation with a learned prior"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "hierarchical importance weighted autoencoders"):
  with members:
    - "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"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "divide and couple using monte carlo variational objectives for posterior approximation"):
  with members:
    - "using large ensembles of control variates for 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 "stochastic variational inference via upper bound"):
  with members:
    - "advances in variational inference"
    - "importance weighted autoencoders"
    - "adam a method 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)