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 "improving explorability in variational inference with annealed variational objectives"):
  Abstract: Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned. We demonstrate the drawbacks of biasing the true posterior to be unimodal, and introduce Annealed Variational Objectives (AVO) into the training of hierarchical variational methods. Inspired by Annealed Importance Sampling, the proposed method facilitates learning by incorporating energy tempering into the optimization objective. In our experiments, we demonstrate our method's robustness to deterministic warm up, and the benefits of encouraging exploration in the latent space.

Includes members:
  Member "iterative amortized inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural enhanced belief propagation on factor graphs")
  Member "inference suboptimality in variational 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 "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial code learning for image generation")
  Member "improving variational auto encoders using householder flow":
    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 "pixelvae a latent variable model for natural images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "weight normalization a simple reparameterization to accelerate training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "importance weighted autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "generating sentences from a continuous space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "paraphrase generation with latent bag of words")

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)