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: "voice conversion using sequence to sequence learning of context posterior probabilities"
  Abstract: Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.

Connected through hyperedge HE-1 (induced by source paper "introduction to voice presentation attack detection and recent advances"):
  with members:
    - "generalization of spoofing countermeasures a case study with asvspoof 2015 and btas 2016 corpora"
    - "attacking speaker recognition with deep generative models"
    - "fooling end to end speaker verification by adversarial examples"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "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"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "sequence to sequence acoustic modeling for voice conversion"):
  with members:
    - "neural voice cloning with a few samples"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "taco vc a single speaker tacotron based voice conversion with limited data"):
  with members:
    - "neural voice cloning with a few samples"
    - "voice conversion with conditional samplernn"
    - "fitting new speakers based on a short untranscribed sample"
    - "tacotron towards end to end speech synthesis"
    - "a wavenet for speech denoising"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "one shot voice conversion by separating speaker and content representations with instance normalization"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "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"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "convs2s vc fully convolutional sequence to sequence voice conversion"):
  with members:
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "tacotron towards end to end speech synthesis"
    - "convolutional sequence to sequence learning"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "rhythm flexible voice conversion without parallel data using cycle gan over phoneme posteriorgram sequences"):
  with members:
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "voice impersonation using generative adversarial networks"
    - "tacotron towards end to end speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "fully character level neural machine translation without explicit segmentation"
    - "wavenet a generative model for raw audio"

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)