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: "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
  Abstract: In this paper, we aim at improving the performance of synthesized speech in statistical parametric speech synthesis (SPSS) based on a generative adversarial network (GAN). In particular, we propose a novel architecture combining the traditional acoustic loss function and the GAN's discriminative loss under a multi-task learning (MTL) framework. The mean squared error (MSE) is usually used to estimate the parameters of deep neural networks, which only considers the numerical difference between the raw audio and the synthesized one. To mitigate this problem, we introduce the GAN as a second task to determine if the input is a natural speech with specific conditions. In this MTL framework, the MSE optimization improves the stability of GAN, and at the same time GAN produces samples with a distribution closer to natural speech. Listening tests show that the multi-task architecture can generate more natural speech that satisfies human perception than the conventional methods.

Connected through hyperedge HE-1 (induced by source paper "a novel framework for selection of gans for an application"):
  with members:
    - "a review on generative adversarial networks algorithms theory and applications"
    - "the six fronts of the generative adversarial networks"
    - "generative adversarial networks a survey and taxonomy"
    - "stabilizing generative adversarial network training a survey"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "improving generalization and stability of generative adversarial networks"
    - "adversarial video generation on complex datasets"

Connected through hyperedge HE-2 (induced by source paper "adversarial encoder multi task decoder for multi stage processes"):
  with members:
    - "multi label learning with missing labels using mixed dependency graphs"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "cnn rnn a unified framework for multi label image classification"
    - "semi supervised learning with deep generative models"
    - "efficient estimation of word representations in vector space"
    - "multi stage classifier design"

Connected through hyperedge HE-3 (induced by source paper "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"):
  with members:
    - "harmonizing maximum likelihood with gans for multimodal conditional generation"
    - "data efficient voice cloning for neural singing synthesis"
    - "gansynth adversarial neural audio synthesis"
    - "adversarial audio synthesis"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "a neural parametric singing synthesizer"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "a new gan based end to end tts training algorithm"):
  with members:
    - "clarinet parallel wave generation in end to end text to speech"
    - "close to human quality tts with transformer"
    - "domain adversarial training for accented speech recognition"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "improving sequence to sequence voice conversion by adding text supervision"):
  with members:
    - "sequence to sequence acoustic modeling for voice conversion"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "tacotron towards end to end speech synthesis"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "wavenet a generative model for raw audio"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "voice impersonation using generative adversarial 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.SD(Sound)