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: "fast generation for convolutional autoregressive models"
  Abstract: Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a na\"{i}ve fashion where redundant computations are unnecessarily repeated. This results in slow generation, making such models infeasible for production environments. In this work, we describe a method to speed up generation in convolutional autoregressive models. The key idea is to cache hidden states to avoid redundant computation. We apply our fast generation method to the Wavenet and PixelCNN++ models and achieve up to $21\times$ and $183\times$ speedups respectively.

Connected through hyperedge HE-1 (induced by source paper "videoflow a conditional flow based model for stochastic video generation"):
  with members:
    - "waveglow a flow based generative network for speech synthesis"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "modulating early visual processing by language"

Connected through hyperedge HE-3 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "graph partition neural networks for semi supervised classification"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "predictive sampling with forecasting autoregressive models"):
  with members:
    - "pixelvae improved pixelvae with discrete prior"
    - "generating long sequences with sparse transformers"
    - "pytorch an imperative style high performance deep learning library"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "spatial pixelcnn generating images from patches"
    - "neural discrete representation learning"
    - "end to end optimized image compression"

Connected through hyperedge HE-5 (induced by source paper "videoflow a flow based generative model for video"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "self supervised visual planning with temporal skip connections"
    - "the reversible residual network backpropagation without storing activations"
    - "attention is all you need"
    - "stochastic variational video prediction"
    - "parallel multiscale autoregressive density estimation"

Connected through hyperedge HE-6 (induced by source paper "flow improving flow based generative models with variational dequantization and architecture design"):
  with members:
    - "neural importance sampling"
    - "efficient neural audio synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "a simple neural attentive meta learner"
    - "pixelsnail an improved autoregressive generative model"
    - "improving variational inference with inverse autoregressive flow"

Connected through hyperedge HE-7 (induced by source paper "neural approximation of an auto regressive process through confidence guided sampling"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "mask predict parallel decoding of conditional masked language models"
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "non monotonic sequential text generation"
    - "attentive neural processes"
    - "conditional neural processes"
    - "blockwise parallel decoding for deep autoregressive models"
    - "neural speed reading via skim rnn"

Connected through hyperedge HE-8 (induced by source paper "probabilistic semantic inpainting with pixel constrained cnns"):
  with members:
    - "attentive neural processes"
    - "image inpainting for irregular holes using partial convolutions"
    - "neural processes"
    - "free form image inpainting with gated convolution"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "generative image inpainting with contextual attention"
    - "image inpainting using multi scale feature image translation"

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)