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: "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.

Connected through hyperedge HE-1 (induced by source paper "effective use of variational embedding capacity in expressive end to end speech synthesis"):
  with members:
    - "location relative attention mechanisms for robust long form speech synthesis"
    - "predicting expressive speaking style from text 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"
    - "fixing a broken elbo"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-2 (induced by source paper "multi reference tacotron by intercross training for style disentangling transfer and control in speech synthesis"):
  with members:
    - "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"
    - "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"
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "deep voice 3 2000 speaker neural text to speech"

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)