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: "waveglow a flow based generative network for speech synthesis"
  Abstract: In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow and WaveNet in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online.

Connected through hyperedge HE-1 (induced by source paper "videoflow a conditional flow based model for stochastic video generation"):
  with members:
    - "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"
    - "fast generation for convolutional autoregressive models"
    - "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 "squeezewave extremely lightweight vocoders for on device speech synthesis"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "efficient neural audio synthesis"
    - "squeezenext hardware aware neural network design"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep voice 3 2000 speaker neural text to speech"

Connected through hyperedge HE-3 (induced by source paper "a neural vocoder with hierarchical generation of amplitude and phase spectra for statistical parametric speech synthesis"):
  with members:
    - "waveform modeling and generation using hierarchical recurrent neural networks for speech bandwidth extension"
    - "clarinet parallel wave generation in end to end text to speech"
    - "efficient neural audio synthesis"
    - "which training methods for gans do actually converge"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "improved training of wasserstein gans"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "pointflow 3d point cloud generation with continuous normalizing flows"):
  with members:
    - "videoflow a flow based generative model for video"
    - "pointwise an unsupervised point wise feature learning network"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep sets"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "point cloud gan"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "high fidelity speech synthesis with adversarial networks"):
  with members:
    - "large scale adversarial representation learning"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "expediting tts synthesis with adversarial vocoding"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (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-7 (induced by source paper "c flow conditional generative flow models for images and 3d point clouds"):
  with members:
    - "revealing scenes by inverting structure from motion reconstructions"
    - "conditional adversarial generative flow for controllable image synthesis"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "neural point based graphics"
    - "fast video object segmentation with spatio temporal gans"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"

Connected through hyperedge HE-8 (induced by source paper "moglow probabilistic and controllable motion synthesis using normalising flows"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "analyzing input and output representations for speech driven gesture generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "fixup initialization residual learning without normalization"
    - "videoflow a flow based generative model for video"
    - "quaternet a quaternion based recurrent model for human motion"
    - "flowavenet a generative flow for raw audio"
    - "efficient neural audio synthesis"

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.SD(Sound)