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: "clarinet parallel wave generation in end to end text to speech"
  Abstract: In this work, we propose a new solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (van den Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a regularized KL divergence between their highly-peaked output distributions. Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation. In addition, we introduce the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch. It significantly outperforms the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet (Ping et al., 2018). We also successfully distill a parallel waveform synthesizer conditioned on the hidden representation in this end-to-end model.

Connected through hyperedge HE-1 (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"
    - "waveglow a flow based generative network for speech synthesis"
    - "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-2 (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-3 (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"
    - "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"
    - "sample efficient adaptive text to speech"

Connected through hyperedge HE-4 (induced by source paper "fastspeech fast robust and controllable text to speech"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "parallel neural text to speech"
    - "waveglow a flow based generative network for speech synthesis"
    - "close to human quality tts with transformer"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "neural speech synthesis with transformer network"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "towards transfer learning for end to end speech synthesis from deep pre trained language models"):
  with members:
    - "feature reinforcement with word embedding and parsing information in neural tts"
    - "an unsupervised autoregressive model for speech representation learning"
    - "waveglow a flow based generative network for speech synthesis"
    - "deep contextualized word representations"
    - "neural speech synthesis with transformer network"
    - "universal language model fine tuning for text classification"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "wavecyclegan2 time domain neural post filter for speech waveform generation"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "efficient neural audio synthesis"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "durian duration informed attention network for multimodal synthesis"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "text based editing of talking head video"
    - "close to human quality tts with transformer"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural speech synthesis with transformer network"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"

Connected through hyperedge HE-8 (induced by source paper "towards generalized speech enhancement with generative adversarial networks"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "segan speech enhancement generative adversarial network"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"

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.CL(Computation and Language)