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: "libritts a corpus derived from librispeech for text to speech"
  Abstract: This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use. It is derived from the original audio and text materials of the LibriSpeech corpus, which has been used for training and evaluating automatic speech recognition systems. The new corpus inherits desired properties of the LibriSpeech corpus while addressing a number of issues which make LibriSpeech less than ideal for text-to-speech work. The released corpus consists of 585 hours of speech data at 24kHz sampling rate from 2,456 speakers and the corresponding texts. Experimental results show that neural end-to-end TTS models trained from the LibriTTS corpus achieved above 4.0 in mean opinion scores in naturalness in five out of six evaluation speakers. The corpus is freely available for download from this http URL.

Connected through hyperedge HE-1 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis"):
  with members:
    - "mosnet deep learning based objective assessment for voice conversion"
    - "speech replay detection with x vector attack embeddings and spectral features"
    - "embeddings for dnn speaker adaptive training"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "automos learning a non intrusive assessor of naturalness of speech"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "close to human quality tts with transformer"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "generating synthetic audio data for attention based speech recognition systems"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "language modeling with deep transformers"
    - "cumulative adaptation for blstm acoustic models"
    - "back translation style data augmentation for end to end asr"
    - "machine speech chain with one shot speaker adaptation"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "training neural speech recognition systems with synthetic speech augmentation"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "multi speaker end to end speech synthesis"):
  with members:
    - "parallel neural text to speech"
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "deep voice 2 multi speaker neural text to speech"

Connected through hyperedge HE-5 (induced by source paper "alternating between spectral and spatial estimation for speech separation and enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "universal sound separation"
    - "unsupervised speech enhancement based on multichannel nmf informed beamforming for noise robust automatic speech recognition"
    - "sdr half baked or well done"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-6 (induced by source paper "mellotron multispeaker expressive voice synthesis by conditioning on rhythm pitch and global style tokens"):
  with members:
    - "adversarially trained end to end korean singing voice synthesis system"
    - "waveglow a flow based generative network for speech synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "state of the art speech recognition with sequence to sequence models"
    - "deep voice 3 2000 speaker neural text to speech"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "predicting prosodic prominence from text with pre trained contextualized word representations"):
  with members:
    - "effects of word embeddings on neural network based pitch accent detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient estimation of word representations in vector space"

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)