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 variational inference with inverse autoregressive flow"
  Abstract: The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive neural network. In experiments, we show that IAF significantly improves upon diagonal Gaussian approximate posteriors. In addition, we demonstrate that a novel type of variational autoencoder, coupled with IAF, is competitive with neural autoregressive models in terms of attained log-likelihood on natural images, while allowing significantly faster synthesis.

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"
    - "improving explorability in variational inference with annealed variational objectives"
    - "vae with a vampprior"
    - "improving variational auto encoders using householder flow"
    - "density estimation using real nvp"
    - "variational lossy autoencoder"

Connected through hyperedge HE-2 (induced by source paper "balancing reconstruction error and kullback leibler divergence in variational autoencoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "from variational to deterministic autoencoders"
    - "sparsity in variational autoencoders"
    - "tackling over pruning in variational autoencoders"
    - "fixing a broken elbo"
    - "vae with a vampprior"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-3 (induced by source paper "lia latently invertible autoencoder with adversarial learning"):
  with members:
    - "generative latent flow a framework for non adversarial image generation"
    - "implicit autoencoders"
    - "large scale adversarial representation learning"
    - "interpreting the latent space of gans for semantic face editing"
    - "adversarial training of partially invertible variational autoencoders"
    - "pioneer networks progressively growing generative autoencoder"
    - "f vaes improve vaes with conditional flows"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-4 (induced by source paper "waveflow a compact flow based model for raw audio"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "high fidelity speech synthesis with adversarial networks"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"
    - "the challenge of realistic music generation modelling raw audio at scale"

Connected through hyperedge HE-5 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"

Connected through hyperedge HE-6 (induced by source paper "dueling decoders regularizing variational autoencoder latent spaces"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning latent dynamics for planning from pixels"
    - "therml the thermodynamics of machine learning"
    - "group normalization"
    - "uncertainty in the variational information bottleneck"
    - "the conditional entropy bottleneck"
    - "cross modal deep variational hand pose estimation"
    - "β vaes can retain label information even at high compression"

Connected through hyperedge HE-7 (induced by source paper "sliced wasserstein generative models"):
  with members:
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "banach wasserstein gan"
    - "generative modeling using the sliced wasserstein distance"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-8 (induced by source paper "parallel neural text to speech"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "efficient neural audio synthesis"
    - "stochastic video generation with a learned prior"

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)