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: "predicting expressive speaking style from text in end to end speech synthesis"
  Abstract: Global Style Tokens (GSTs) are a recently-proposed method to learn latent disentangled representations of high-dimensional data. GSTs can be used within Tacotron, a state-of-the-art end-to-end text-to-speech synthesis system, to uncover expressive factors of variation in speaking style. In this work, we introduce the Text-Predicted Global Style Token (TP-GST) architecture, which treats GST combination weights or style embeddings as "virtual" speaking style labels within Tacotron. TP-GST learns to predict stylistic renderings from text alone, requiring neither explicit labels during training nor auxiliary inputs for inference. We show that, when trained on a dataset of expressive speech, our system generates audio with more pitch and energy variation than two state-of-the-art baseline models. We further demonstrate that TP-GSTs can synthesize speech with background noise removed, and corroborate these analyses with positive results on human-rated listener preference audiobook tasks. Finally, we demonstrate that multi-speaker TP-GST models successfully factorize speaker identity and speaking style. We provide a website with audio samples for each of our findings.

Connected through hyperedge HE-1 (induced by source paper "in other news a bi style text to speech model for synthesizing newscaster voice with limited data"):
  with members:
    - "neural voice cloning with a few samples"
    - "speaking style adaptation in text to speech synthesis using sequence to sequence models with attention"
    - "effect of data reduction on sequence to sequence neural tts"
    - "close to human quality tts with transformer"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "learning to groove with inverse sequence transformations"):
  with members:
    - "counterpoint by convolution"
    - "video to video synthesis"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical neural story generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"

Connected through hyperedge HE-3 (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"
    - "learning latent representations for style control and transfer in end to end speech synthesis"
    - "robust and fine grained prosody control of 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-4 (induced by source paper "dynamic prosody generation for speech synthesis using linguistics driven acoustic embedding selection"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "exploiting syntactic features in a parsed tree to improve end to end tts"
    - "an empirical analysis of the correlation of syntax and prosody"
    - "effects of word embeddings on neural network based pitch accent detection"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable speech synthesis"

Connected through hyperedge HE-5 (induced by source paper "libritts a corpus derived from librispeech for text to speech"):
  with members:
    - "neural voice cloning with a few samples"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "efficient neural audio synthesis"
    - "representation mixing for tts synthesis"
    - "sample efficient adaptive text to speech"
    - "neural speech synthesis with transformer network"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "voice imitating text to speech neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning latent representations for style control and transfer in end to end speech synthesis"):
  with members:
    - "close to human quality tts with transformer"
    - "neural speech synthesis with transformer network"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "learning latent representations for speech generation and transformation"
    - "wavenet a generative model for raw audio"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-7 (induced by source paper "robust and fine grained prosody control of end to end speech synthesis"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)