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: "robust and fine grained prosody control of end to end speech synthesis"
  Abstract: We propose prosody embeddings for emotional and expressive speech synthesis networks. The proposed methods introduce temporal structures in the embedding networks, thus enabling fine-grained control of the speaking style of the synthesized speech. The temporal structures can be designed either on the speech side or the text side, leading to different control resolutions in time. The prosody embedding networks are plugged into end-to-end speech synthesis networks and trained without any other supervision except for the target speech for synthesizing. It is demonstrated that the prosody embedding networks learned to extract prosodic features. By adjusting the learned prosody features, we could change the pitch and amplitude of the synthesized speech both at the frame level and the phoneme level. We also introduce the temporal normalization of prosody embeddings, which shows better robustness against speaker perturbations during prosody transfer tasks.

Connected through hyperedge HE-1 (induced by source paper "direct speech to speech translation with a sequence to sequence model"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "end to end spoken language translation"
    - "tied multitask learning for neural speech translation"
    - "neural voice cloning with a few samples"
    - "end to end automatic speech translation of audiobooks"
    - "efficient neural audio synthesis"
    - "conditional end to end audio transforms"

Connected through hyperedge HE-2 (induced by source paper "effective use of variational embedding capacity in expressive end to end speech synthesis"):
  with members:
    - "location relative attention mechanisms for robust long form speech synthesis"
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "learning latent representations for style control and transfer in end to end speech synthesis"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "fixing a broken elbo"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "teacher student training for robust tacotron based tts"):
  with members:
    - "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"
    - "fastspeech fast robust and controllable text to speech"
    - "a new gan based end to end tts training algorithm"
    - "generalization in generation a closer look at exposure bias"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "online and linear time attention by enforcing monotonic alignments"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "location relative attention mechanisms for robust long form speech synthesis"):
  with members:
    - "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"
    - "effective use of variational embedding capacity in expressive end to end speech synthesis"
    - "efficient neural audio synthesis"
    - "representation mixing for tts synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "online and linear time attention by enforcing monotonic alignments"
    - "tacotron towards end to end speech synthesis"
    - "attention based models for speech recognition"

Connected through hyperedge HE-5 (induced by source paper "prosody transfer in neural text to speech using global pitch and loudness features"):
  with members:
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "efficient neural audio 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"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "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.CL(Computation and Language)