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 synthesis for in the wild speakers via a phonological loop"
  Abstract: We present a new neural text to speech method that is able to transform text to speech in voices that are sampled in the wild. Unlike other text to speech systems, our solution is able to deal with unconstrained samples obtained from public speeches. 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. Lastly, the speakers are similarly represented by a short vector that can also be fitted to new speakers and variability in the generated speech is achieved by priming the buffer prior to generating the audio. Experimental results on two datasets demonstrate convincing multi-speaker and in-the-wild capabilities. In order to promote reproducibility, we release our source code and models: PyTorch code and sample audio files are available at ytaigman.github.io/loop.

Connected through hyperedge HE-1 (induced by source paper "css10 a collection of single speaker speech datasets for 10 languages"):
  with members:
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "jsut corpus free large scale japanese speech corpus for end to end speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

Connected through hyperedge HE-2 (induced by source paper "neural voice cloning with a few samples"):
  with members:
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep voice real time neural text to speech"
    - "regularizing and optimizing lstm language models"
    - "deep speaker an end to end neural speaker embedding system"
    - "embedding based speaker adaptive training of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "representation mixing for tts synthesis"):
  with members:
    - "clarinet parallel wave generation in end to end text to speech"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "a neural representation of sketch drawings"
    - "recurrent dropout without memory loss"

Connected through hyperedge HE-4 (induced by source paper "predicting expressive speaking style from text in end to end speech synthesis"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "tacotron towards end to end speech synthesis"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "emotional end to end neural speech synthesizer"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "listening while speaking speech chain by deep learning"
    - "tacotron towards end to end speech synthesis"
    - "fully character level neural machine translation without explicit segmentation"
    - "wavenet a generative model for raw audio"
    - "sequence to sequence learning with neural networks"

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)