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: "adversarial audio synthesis"
  Abstract: Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiments demonstrate that, without labels, WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.

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"
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans"):
  with members:
    - "naomi non autoregressive multiresolution sequence imputation"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "improved training of wasserstein gans"
    - "on the discrimination generalization tradeoff in gans"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "f divergence inequalities"

Connected through hyperedge HE-3 (induced by source paper "reliable fidelity and diversity metrics for generative models"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "pros and cons of gan evaluation measures"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "nsml meet the mlaas platform with a real world case study"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep image prior"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "high fidelity speech synthesis with adversarial networks"):
  with members:
    - "large scale adversarial representation learning"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "expediting tts synthesis with adversarial vocoding"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "waveglow a flow based generative network for speech synthesis"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "fast and provable admm for learning with generative priors"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "algorithmic aspects of inverse problems using generative models"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "score and lyrics free singing voice generation"):
  with members:
    - "dilated convolution with dilated gru for music source separation"
    - "data efficient voice cloning for neural singing synthesis"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "gansynth adversarial neural audio synthesis"
    - "an overview of lead and accompaniment separation in music"
    - "group normalization"
    - "revisiting singing voice detection a quantitative review and the future outlook"

Connected through hyperedge HE-8 (induced by source paper "deep long audio inpainting"):
  with members:
    - "deep video inpainting"
    - "adversarial generation of time frequency features with application in audio synthesis"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "learnable gated temporal shift module for deep video inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "a context encoder for audio inpainting"
    - "efficient neural audio 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.SD(Sound)