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: "deep voice real time neural text to speech"
  Abstract: We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations.

Connected through hyperedge HE-1 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"

Connected through hyperedge HE-2 (induced by source paper "vision infused deep audio inpainting"):
  with members:
    - "compound domain adaptation in an open world"
    - "recursive visual sound separation using minus plus net"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"

Connected through hyperedge HE-3 (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"
    - "waveglow a flow based generative network for speech synthesis"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"

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 "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"

Connected through hyperedge HE-6 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "neural voice cloning with a few samples"

Connected through hyperedge HE-7 (induced by source paper "crossbow scaling deep learning with small batch sizes on multi gpu servers"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ray a distributed framework for emerging ai applications"

Connected through hyperedge HE-8 (induced by source paper "semi supervised generative modeling for controllable speech synthesis"):
  with members:
    - "effective use of variational embedding capacity in expressive end to end speech synthesis"
    - "fastspeech fast robust and controllable text to speech"
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"

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)