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 "the zero resource speech challenge 2019 tts without t"):
  Abstract: We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels. Participants must discover subword units in an unsupervised way (using the Unit Discovery dataset) and align them to the voice recordings in a way that works best for the purpose of synthesizing novel utterances from novel speakers, similar to the target speaker's voice. We describe the metrics used for evaluation, a baseline system consisting of unsupervised subword unit discovery plus a standard TTS system, and a topline TTS using gold phoneme transcriptions. We present an overview of the 19 submitted systems from 10 teams and discuss the main results.

Includes members:
  Member "unsupervised acoustic unit discovery for speech synthesis using discrete latent variable neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vq wav2vec self supervised learning of discrete speech representations")
  Member "unsupervised end to end learning of discrete linguistic units for voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "unsupervised speech representation learning using wavenet autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from abstract items to latent spaces to observed data and back compositional variational auto encoder")
  Member "close to human quality tts with transformer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit")
  Member "bayesian models for unit discovery on a very low resource language":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian subspace hidden markov model for acoustic unit discovery")
  Member "voice impersonation using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic forecasting of sensory data with generative adversarial networks forgan")
  Member "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")

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)