human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "neural voice cloning with a few samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "speech2phone a multilingual and text independent speaker identification model")
  Member "style tokens unsupervised style modeling control and transfer in end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "sample efficient adaptive text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "voxceleb2 deep speaker recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "everybody s talkin let me talk as you want")
  Member "fitting new speakers based on a short untranscribed sample":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "towards end to end prosody transfer for expressive speech synthesis with tacotron":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "et gan cross language emotion transfer based on cycle consistent generative adversarial networks")
  Member "voiceloop voice fitting and synthesis via a phonological loop":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "voxceleb a large scale speaker identification dataset":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "first order motion model for image animation")

Question: Please tell me which class the source paper 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)