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: "on using backpropagation for speech texture generation and voice conversion"
  Abstract: Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances. Similar to image texture synthesis and neural style transfer, the system works by optimizing a cost function with respect to the input waveform samples. To this end we use a differentiable mel-filterbank feature extraction pipeline and train a convolutional CTC speech recognition network. Our system is able to extract speaker characteristics from very limited amounts of target speaker data, as little as a few seconds, and can be used to generate realistic speech babble or reconstruct an utterance in a different voice.

Connected through hyperedge HE-1 (induced by source paper "unsupervised speech representation learning using wavenet autoencoders"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "disentangled sequential autoencoder"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "conditional end to end audio transforms"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "neural discrete representation learning"
    - "a persona based neural conversation model"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "synthesizing diverse high quality audio textures"):
  with members:
    - "audio style transfer"
    - "understanding deep representations through random weights"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "texture synthesis using shallow convolutional networks with random filters"
    - "improved techniques for training gans"
    - "a powerful generative model using random weights for the deep image representation"
    - "a learned representation for artistic style"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "towards end to end prosody transfer for expressive speech synthesis with tacotron"):
  with members:
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "tacotron towards end to end speech synthesis"
    - "uncovering latent style factors for expressive speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "neural discrete representation learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)