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: "uncovering latent style factors for expressive speech synthesis"
  Abstract: Prosodic modeling is a core problem in speech synthesis. The key challenge is producing desirable prosody from textual input containing only phonetic information. In this preliminary study, we introduce the concept of "style tokens" in Tacotron, a recently proposed end-to-end neural speech synthesis model. Using style tokens, we aim to extract independent prosodic styles from training data. We show that without annotation data or an explicit supervision signal, our approach can automatically learn a variety of prosodic variations in a purely data-driven way. Importantly, each style token corresponds to a fixed style factor regardless of the given text sequence. As a result, we can control the prosodic style of synthetic speech in a somewhat predictable and globally consistent way.

Connected through hyperedge HE-1 (induced by source paper "interactive text to speech via semi supervised style transfer learning"):
  with members:
    - "visualization and interpretation of latent spaces for controlling expressive speech synthesis through audio analysis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multimodal speech emotion recognition using audio and text"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "attention is all you need"
    - "wavenet a generative model for raw audio"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "towards end to end prosody transfer for expressive speech synthesis with tacotron"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "tacotron towards end to end speech synthesis"
    - "on using backpropagation for speech texture generation and voice conversion"
    - "deep voice 2 multi speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "neural discrete representation learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "expressive speech synthesis via modeling expressions with variational autoencoder"):
  with members:
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "tacotron towards end to end speech synthesis"
    - "learning latent representations for speech generation and transformation"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "pixelcnn models with auxiliary variables for natural image modeling"
    - "pixelvae a latent variable model for natural images"
    - "wavenet a generative model for raw audio"
    - "generating sentences from a continuous 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.CL(Computation and Language)