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: "voice imitating text to speech neural networks"
  Abstract: We propose a neural text-to-speech (TTS) model that can imitate a new speaker's voice using only a small amount of speech sample. We demonstrate voice imitation using only a 6-seconds long speech sample without any other information such as transcripts. Our model also enables voice imitation instantly without additional training of the model. We implemented the voice imitating TTS model by combining a speaker embedder network with a state-of-the-art TTS model, Tacotron. The speaker embedder network takes a new speaker's speech sample and returns a speaker embedding. The speaker embedding with a target sentence are fed to Tacotron, and speech is generated with the new speaker's voice. We show that the speaker embeddings extracted by the speaker embedder network can represent the latent structure in different voices. The generated speech samples from our model have comparable voice quality to the ones from existing multi-speaker TTS models.

Connected through hyperedge HE-1 (induced by source paper "libritts a corpus derived from librispeech for text to speech"):
  with members:
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "neural voice cloning with a few samples"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "efficient neural audio synthesis"
    - "representation mixing for tts synthesis"
    - "sample efficient adaptive text to speech"
    - "neural speech synthesis with transformer network"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-2 (induced by source paper "unsupervised polyglot text to speech"):
  with members:
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "convolutional sequence to sequence learning"
    - "wavenet a generative model for raw audio"

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)