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 "hierarchical importance weighted autoencoders"):
  Abstract: Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation. The hope is that the proposals would coordinate to make up for the error made by one another to reduce the variance of the importance estimator. Theoretically, we analyze the condition under which convergence of the estimator variance can be connected to convergence of the lower bound. Empirically, we confirm that maximization of the lower bound does implicitly minimize variance. Further analysis shows that this is a result of negative correlation induced by the proposed hierarchical meta sampling scheme, and performance of inference also improves when the number of samples increases.

Includes members:
  Member "importance weighting and variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning discrete distributions by dequantization")
  Member "neural importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flow improving flow based generative models with variational dequantization and architecture design")
  Member "differentiable antithetic sampling for variance reduction in stochastic variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "note on the bias and variance of variational inference")
  Member "inference suboptimality in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "doubly reparameterized gradient estimators for monte carlo objectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization")
  Member "filtering variational objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "variational inference for monte carlo objectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")

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)