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: "fitting new speakers based on a short untranscribed sample"
  Abstract: Learning-based Text To Speech systems have the potential to generalize from one speaker to the next and thus require a relatively short sample of any new voice. However, this promise is currently largely unrealized. We present a method that is designed to capture a new speaker from a short untranscribed audio sample. This is done by employing an additional network that given an audio sample, places the speaker in the embedding space. This network is trained as part of the speech synthesis system using various consistency losses. Our results demonstrate a greatly improved performance on both the dataset speakers, and, more importantly, when fitting new voices, even from very short samples.

Connected through hyperedge HE-1 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "parallel neural text to speech"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "efficient neural audio synthesis"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-3 (induced by source paper "direct speech to speech translation with a sequence to sequence model"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "end to end spoken language translation"
    - "tied multitask learning for neural speech translation"
    - "neural voice cloning with a few samples"
    - "end to end automatic speech translation of audiobooks"
    - "efficient neural audio synthesis"
    - "conditional end to end audio transforms"

Connected through hyperedge HE-4 (induced by source paper "sequence to sequence acoustic modeling for voice conversion"):
  with members:
    - "neural voice cloning with a few samples"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "unsupervised singing voice conversion"):
  with members:
    - "unsupervised polyglot text to speech"
    - "data efficient voice cloning for neural singing synthesis"
    - "sample efficient adaptive text to speech"
    - "a universal music translation network"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "mixup beyond empirical risk minimization"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

Connected through hyperedge HE-6 (induced by source paper "tts skins speaker conversion via asr"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "sample efficient adaptive text to speech"
    - "a universal music translation network"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep voice 2 multi speaker neural text to speech"

Connected through hyperedge HE-7 (induced by source paper "taco vc a single speaker tacotron based voice conversion with limited data"):
  with members:
    - "neural voice cloning with a few samples"
    - "voice conversion with conditional samplernn"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "tacotron towards end to end speech synthesis"
    - "a wavenet for speech denoising"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-8 (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"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable 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.LG(Machine Learning)