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: "transfer learning from speaker verification to multispeaker text to speech synthesis"
  Abstract: We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.

Connected through hyperedge HE-1 (induced by source paper "parallel neural text to speech"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural voice cloning with a few samples"
    - "clarinet parallel wave generation in end to end text to speech"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "efficient neural audio synthesis"
    - "stochastic video generation with a learned prior"
    - "sample efficient adaptive text to speech"

Connected through hyperedge HE-2 (induced by source paper "transfer learning with dynamic distribution adaptation"):
  with members:
    - "fedhealth a federated transfer learning framework for wearable healthcare"
    - "decomposition based transfer distance metric learning for image classification"
    - "easy transfer learning by exploiting intra domain structures"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "cross position activity recognition with stratified transfer learning"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "libritts a corpus derived from librispeech for text to speech"):
  with members:
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "neural voice cloning with a few samples"
    - "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-4 (induced by source paper "tts skins speaker conversion via asr"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "sample efficient adaptive text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "a universal music translation network"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep voice 2 multi speaker neural text to speech"

Connected through hyperedge HE-5 (induced by source paper "a transfer learning method for goal recognition exploiting cross domain spatial features"):
  with members:
    - "convolutional neural networks with layer reuse"
    - "deep learning for sensor based activity recognition a survey"
    - "human action recognition and prediction a survey"
    - "a survey on deep transfer learning"
    - "lstm based goal recognition in latent space"
    - "heuristic online goal recognition in continuous domains"
    - "latent intention dialogue models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"

Connected through hyperedge HE-6 (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"
    - "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"
    - "tacotron towards end to end speech synthesis"

Connected through hyperedge HE-7 (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"
    - "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"
    - "emotional end to end neural speech synthesizer"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "low bit rate speech coding with vq vae and a wavenet decoder"):
  with members:
    - "sample efficient adaptive text to speech"
    - "end to end optimized speech coding with deep neural networks"
    - "neural discrete representation learning"
    - "end to end optimized image compression"
    - "composition of deep and spiking neural networks for very low bit rate speech coding"
    - "wavenet a generative model for raw audio"
    - "speex a free codec for free speech"

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)