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: "expressive speech synthesis via modeling expressions with variational autoencoder"
  Abstract: Recent advances in neural autoregressive models have improve the performance of speech synthesis (SS). However, as they lack the ability to model global characteristics of speech (such as speaker individualities or speaking styles), particularly when these characteristics have not been labeled, making neural autoregressive SS systems more expressive is still an open issue. In this paper, we propose to combine VoiceLoop, an autoregressive SS model, with Variational Autoencoder (VAE). This approach, unlike traditional autoregressive SS systems, uses VAE to model the global characteristics explicitly, enabling the expressiveness of the synthesized speech to be controlled in an unsupervised manner. Experiments using the VCTK and Blizzard2012 datasets show the VAE helps VoiceLoop to generate higher quality speech and to control the expressions in its synthesized speech by incorporating global characteristics into the speech generating process.

Connected through hyperedge HE-1 (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"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"

Connected through hyperedge HE-2 (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"
    - "close to human quality tts with transformer"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "exploiting syntactic features in a parsed tree to improve end to end tts"
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "an empirical analysis of the correlation of syntax and prosody"
    - "effects of word embeddings on neural network based pitch accent detection"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-4 (induced by source paper "multi reference tacotron by intercross training for style disentangling transfer and control in speech synthesis"):
  with members:
    - "learning latent representations for style control and transfer in end to end speech synthesis"
    - "close to human quality tts with transformer"
    - "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"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "deep voice 3 2000 speaker neural text to speech"

Connected through hyperedge HE-5 (induced by source paper "learning to speak fluently in a foreign language multilingual speech synthesis and cross language voice cloning"):
  with members:
    - "unsupervised polyglot text to speech"
    - "neural voice cloning with a few samples"
    - "efficient neural audio synthesis"
    - "representation mixing for tts synthesis"
    - "sample efficient adaptive text to speech"
    - "learning pronunciation from a foreign language in speech synthesis networks"
    - "fitting new speakers based on a short untranscribed sample"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-6 (induced by source paper "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"
    - "wavenet a generative model for raw audio"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-7 (induced by source paper "prosody transfer in neural text to speech using global pitch and loudness features"):
  with members:
    - "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"
    - "robust and fine grained prosody control of end to end speech synthesis"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-8 (induced by source paper "visualization and interpretation of latent spaces for controlling expressive speech synthesis through audio analysis"):
  with members:
    - "exploring transfer learning for low resource emotional tts"
    - "the emotional voices database towards controlling the emotion dimension in voice generation systems"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "tacotron towards end to end speech 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.CL(Computation and Language)