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: "close to human quality tts with transformer"
  Abstract: Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves the training efficiency. Meanwhile, any two inputs at different times are connected directly by self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).

Connected through hyperedge HE-1 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "jvs corpus free japanese multi speaker voice corpus"
    - "libritts a corpus derived from librispeech for text to speech"
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "back translation style data augmentation for end to end asr"
    - "waveglow a flow based generative network for speech synthesis"
    - "espnet end to end speech processing toolkit"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"

Connected through hyperedge HE-2 (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"
    - "clarinet parallel wave generation in end to end text to speech"
    - "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-3 (induced by source paper "probing the phonetic and phonological knowledge of tones in mandarin tts models"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "using priming to uncover the organization of syntactic representations in neural language models"
    - "fastspeech fast robust and controllable text to speech"
    - "towards transfer learning for end to end speech synthesis from deep pre trained language models"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "representation learning for discovering phonemic tone contours"
    - "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"
    - "pre training with whole word masking for chinese bert"

Connected through hyperedge HE-4 (induced by source paper "in other news a bi style text to speech model for synthesizing newscaster voice with limited data"):
  with members:
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "neural voice cloning with a few samples"
    - "speaking style adaptation in text to speech synthesis using sequence to sequence models with attention"
    - "effect of data reduction on sequence to sequence neural tts"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "attention forcing for sequence to sequence model training"):
  with members:
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural speech synthesis with transformer network"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "tacotron towards end to end speech synthesis"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-6 (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"
    - "clarinet parallel wave generation in end to end text to speech"
    - "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-7 (induced by source paper "the zero resource speech challenge 2019 tts without t"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "bayesian models for unit discovery on a very low resource language"
    - "voice impersonation using generative adversarial networks"
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "listening while speaking speech chain by deep learning"

Connected through hyperedge HE-8 (induced by source paper "a new gan based end to end tts training algorithm"):
  with members:
    - "clarinet parallel wave generation in end to end text to speech"
    - "domain adversarial training for accented speech recognition"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"
    - "attention is all you need"
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "tacotron towards end to end speech synthesis"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

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)