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: "voiceloop voice fitting and synthesis via a phonological loop"
  Abstract: We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild. Unlike other systems, our solution is able to deal with unconstrained voice samples and without requiring aligned phonemes or linguistic features. The network architecture is simpler than those in the existing literature and is based on a novel shifting buffer working memory. The same buffer is used for estimating the attention, computing the output audio, and for updating the buffer itself. The input sentence is encoded using a context-free lookup table that contains one entry per character or phoneme. The speakers are similarly represented by a short vector that can also be fitted to new identities, even with only a few samples. Variability in the generated speech is achieved by priming the buffer prior to generating the audio. Experimental results on several datasets demonstrate convincing capabilities, making TTS accessible to a wider range of applications. In order to promote reproducibility, we release our source code and models.

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"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "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 "waveflow a compact flow based model for raw audio"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "high fidelity speech synthesis with adversarial networks"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"
    - "the challenge of realistic music generation modelling raw audio at scale"

Connected through hyperedge HE-3 (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-4 (induced by source paper "semi supervised generative modeling for controllable speech synthesis"):
  with members:
    - "effective use of variational embedding capacity in expressive end to end speech synthesis"
    - "fastspeech fast robust and controllable text to speech"
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "jejueo datasets for machine translation and speech synthesis"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "css10 a collection of single speaker speech datasets for 10 languages"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "universal neural machine translation for extremely low resource languages"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-6 (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"
    - "fitting new speakers based on a short untranscribed sample"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "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-7 (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"
    - "fitting new speakers based on a short untranscribed sample"
    - "a universal music translation network"
    - "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-8 (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"
    - "learning latent representations for style control and transfer 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"
    - "fixing a broken elbo"
    - "tacotron towards end to end 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)