human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning latent representations for style control and transfer in end to end speech synthesis"):
  Abstract: In this paper, we introduce the Variational Autoencoder (VAE) to an end-to-end speech synthesis model, to learn the latent representation of speaking styles in an unsupervised manner. The style representation learned through VAE shows good properties such as disentangling, scaling, and combination, which makes it easy for style control. Style transfer can be achieved in this framework by first inferring style representation through the recognition network of VAE, then feeding it into TTS network to guide the style in synthesizing speech. To avoid Kullback-Leibler (KL) divergence collapse in training, several techniques are adopted. Finally, the proposed model shows good performance of style control and outperforms Global Style Token (GST) model in ABX preference tests on style transfer.

Includes members:
  Member "predicting expressive speaking style from text in end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "in other news a bi style text to speech model for synthesizing newscaster voice with limited data")
  Member "close to human quality tts with transformer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit")
  Member "neural speech synthesis with transformer network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised neural architecture search")
  Member "expressive speech synthesis via modeling expressions with variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised generative modeling for controllable speech synthesis")
  Member "towards end to end prosody transfer for expressive speech synthesis with tacotron":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "et gan cross language emotion transfer based on cycle consistent generative adversarial networks")
  Member "deep voice 3 scaling text to speech with convolutional sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised neural architecture search")
  Member "learning latent representations for speech generation and transformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep autotuner a pitch correcting network for singing performances")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")

Question: Please tell me which class the source paper 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)