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: "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.

Connected through hyperedge HE-1 (induced by source paper "gradient boosted flows"):
  with members:
    - "inference suboptimality in variational autoencoders"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "gaussian process prior variational autoencoders"
    - "vae with a vampprior"
    - "improving variational auto encoders using householder flow"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"
    - "variational lossy autoencoder"

Connected through hyperedge HE-2 (induced by source paper "variational auto encoder not all failures are equal"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "fixing a broken elbo"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "pixelvae a latent variable model for natural images"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "the usual suspects reassessing blame for vae posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "variational autoencoders pursue pca directions by accident"
    - "tackling over pruning in variational autoencoders"
    - "hidden talents of the variational autoencoder"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-4 (induced by source paper "don t blame the elbo a linear vae perspective on posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "preventing posterior collapse with delta vaes"
    - "loss landscapes of regularized linear autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "inference suboptimality in variational autoencoders"
    - "variational autoencoders pursue pca directions by accident"

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)