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: "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
  Abstract: This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate converted speech signals quickly enough to allow real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic-sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.

Connected through hyperedge HE-1 (induced by source paper "voice conversion for whispered speech synthesis"):
  with members:
    - "glottal source processing from analysis to applications"
    - "lstm based whisper detection"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multivariate information adversarial ensemble for scalable joint distribution matching"):
  with members:
    - "video to video synthesis"
    - "geometry score a method for comparing generative adversarial networks"
    - "soft gated warping gan for pose guided person image synthesis"
    - "modular generative adversarial networks"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "style transfer from non parallel text by cross alignment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "it takes only two adversarial generator encoder networks"

Connected through hyperedge HE-3 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "waveglow a flow based generative network for speech synthesis"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "neural voice cloning with a few samples"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-4 (induced by source paper "a style transfer approach to source separation"):
  with members:
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "unsupervised training of neural mask based beamforming"
    - "audio style transfer"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"

Connected through hyperedge HE-5 (induced by source paper "cyclegan vc2 improved cyclegan based non parallel voice conversion"):
  with members:
    - "convs2s vc fully convolutional sequence to sequence voice conversion"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "unsupervised cross domain image generation"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "wavecyclegan2 time domain neural post filter for speech waveform generation"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "waveglow a flow based generative network for speech synthesis"
    - "clarinet parallel wave generation in end to end text to speech"
    - "efficient neural audio synthesis"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "crossmodal voice conversion"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "cmcgan a uniform framework for cross modal visual audio mutual generation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "visual to sound generating natural sound for videos in the wild"
    - "deep cross modal audio visual generation"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-8 (induced by source paper "one shot voice conversion by separating speaker and content representations with instance normalization"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "machine speech chain with one shot speaker adaptation"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "rhythm flexible voice conversion without parallel data using cycle gan over phoneme posteriorgram sequences"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "tacotron towards end to end speech 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)