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: "learning latent representations for speech generation and transformation"
  Abstract: An ability to model a generative process and learn a latent representation for speech in an unsupervised fashion will be crucial to process vast quantities of unlabelled speech data. Recently, deep probabilistic generative models such as Variational Autoencoders (VAEs) have achieved tremendous success in modeling natural images. In this paper, we apply a convolutional VAE to model the generative process of natural speech. We derive latent space arithmetic operations to disentangle learned latent representations. We demonstrate the capability of our model to modify the phonetic content or the speaker identity for speech segments using the derived operations, without the need for parallel supervisory data.

Connected through hyperedge HE-1 (induced by source paper "deep autotuner a pitch correcting network for singing performances"):
  with members:
    - "deep learning for singing processing achievements challenges and impact on singers and listeners"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech"):
  with members:
    - "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks"
    - "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese"
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "unsupervised end to end learning of discrete linguistic units for voice conversion"
    - "language learning using speech to image retrieval"
    - "towards visually grounded sub word speech unit discovery"
    - "the zero resource speech challenge 2019 tts without t"
    - "word recognition competition and activation in a model of visually grounded speech"

Connected through hyperedge HE-3 (induced by source paper "an unsupervised autoregressive model for speech representation learning"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "unspeech unsupervised speech context embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "dissecting contextual word embeddings architecture and representation"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-4 (induced by source paper "contextual joint factor acoustic embeddings"):
  with members:
    - "unspeech unsupervised speech context embeddings"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "learning word embeddings from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "context encoders feature learning by inpainting"
    - "sequence to sequence rnns for text summarization"

Connected through hyperedge HE-5 (induced by source paper "audio source separation using variational autoencoders and weak class supervision"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "supervised speech separation based on deep learning an overview"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "classical music prediction and composition by means of variational autoencoders"):
  with members:
    - "non monotonic sequential text generation"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "generative timbre spaces regularizing variational auto encoders with perceptual metrics"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-7 (induced by source paper "deep autotuner a data driven approach to natural sounding pitch correction for singing voice in karaoke performances"):
  with members:
    - "deep learning for singing processing achievements challenges and impact on singers and listeners"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "learning disentangled representations for timber and pitch in music audio"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "exploring disentangled feature representation beyond face identification"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "learning to decompose and disentangle representations for video prediction"
    - "performancenet score to audio music generation with multi band convolutional residual network"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

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)