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: "emotional end to end neural speech synthesizer"
  Abstract: In this paper, we introduce an emotional speech synthesizer based on the recent end-to-end neural model, named Tacotron. Despite its benefits, we found that the original Tacotron suffers from the exposure bias problem and irregularity of the attention alignment. Later, we address the problem by utilization of context vector and residual connection at recurrent neural networks (RNNs). Our experiments showed that the model could successfully train and generate speech for given emotion labels.

Connected through hyperedge HE-1 (induced by source paper "et gan cross language emotion transfer based on cycle consistent generative adversarial networks"):
  with members:
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "voxceleb2 deep speaker recognition"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voxceleb a large scale speaker identification dataset"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cnn architectures for large scale audio classification"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "visualization and interpretation of latent spaces for controlling expressive speech synthesis through audio analysis"):
  with members:
    - "exploring transfer learning for low resource emotional tts"
    - "the emotional voices database towards controlling the emotion dimension in voice generation systems"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"

Connected through hyperedge HE-3 (induced by source paper "eg gan cross language emotion gain synthesis based on cycle consistent adversarial networks"):
  with members:
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "voxceleb2 deep speaker recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "nips 2016 tutorial generative adversarial networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "exploring transfer learning for low resource emotional tts"):
  with members:
    - "the emotional voices database towards controlling the emotion dimension in voice generation systems"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "efficient neural audio synthesis"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "voice imitating text to speech neural networks"):
  with members:
    - "deep voice real time neural text to speech"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)