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: "fast spectrogram inversion using multi head convolutional neural networks"
  Abstract: We propose the multi-head convolutional neural network (MCNN) for waveform synthesis from spectrograms. Nonlinear interpolation in MCNN is employed with transposed convolution layers in parallel heads. MCNN enables significantly better utilization of modern multi-core processors than commonly used iterative algorithms like Griffin–Lim, and yields very fast (more than 300 × real time) runtime. For training of MCNN, we use a large-scale speech recognition dataset and losses defined on waveforms that are related to perceptual audio quality. We demonstrate that MCNN constitutes a very promising approach for high-quality speech synthesis, without any iterative algorithms or autoregression in computations.

Connected through hyperedge HE-1 (induced by source paper "ddsp differentiable digital signal processing"):
  with members:
    - "universal audio synthesizer control with normalizing flows"
    - "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
    - "gansynth adversarial neural audio synthesis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "sing symbol to instrument neural generator"
    - "efficient neural audio synthesis"
    - "adversarial audio synthesis"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "single channel speech enhancement using temporal convolutional recurrent neural networks"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "supervised speech separation based on deep learning an overview"
    - "a wavenet for speech denoising"
    - "wavenet a generative model for raw audio"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "parallel neural text to speech"):
  with members:
    - "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"
    - "sample efficient adaptive text to speech"

Connected through hyperedge HE-4 (induced by source paper "neural drum machine an interactive system for real time synthesis of drum sounds"):
  with members:
    - "variation network learning high level attributes for controlled input manipulation"
    - "waveglow a flow based generative network for speech synthesis"
    - "generative timbre spaces regularizing variational auto encoders with perceptual metrics"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "adversarial audio synthesis"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-5 (induced by source paper "temporal convolution for real time keyword spotting on mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mnasnet platform aware neural architecture search for mobile"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "small footprint keyword spotting using deep neural network and connectionist temporal classifier"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-6 (induced by source paper "assisted sound sample generation with musical conditioning in adversarial auto encoders"):
  with members:
    - "tackling over pruning in variational autoencoders"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "infovae information maximizing variational autoencoders"
    - "towards deeper understanding of variational autoencoding models"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "film visual reasoning with a general conditioning layer"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-7 (induced by source paper "waveglow a flow based generative network for speech synthesis"):
  with members:
    - "image transformer"
    - "clarinet parallel wave generation in end to end text to speech"
    - "efficient neural audio synthesis"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 2 multi speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

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)