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 "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019"):
  Abstract: We describe our submitted system for the ZeroSpeech Challenge 2019. The current challenge theme addresses the difficulty of constructing a speech synthesizer without any text or phonetic labels and requires a system that can (1) discover subword units in an unsupervised way, and (2) synthesize the speech with a target speaker's voice. Moreover, the system should also balance the discrimination score ABX, the bit-rate compression rate, and the naturalness and the intelligibility of the constructed voice. To tackle these problems and achieve the best trade-off, we utilize a vector quantized variational autoencoder (VQ-VAE) and a multi-scale codebook-to-spectrogram (Code2Spec) inverter trained by mean square error and adversarial loss. The VQ-VAE extracts the speech to a latent space, forces itself to map it into the nearest codebook and produces compressed representation. Next, the inverter generates a magnitude spectrogram to the target voice, given the codebook vectors from VQ-VAE. In our experiments, we also investigated several other clustering algorithms, including K-Means and GMM, and compared them with the VQ-VAE result on ABX scores and bit rates. Our proposed approach significantly improved the intelligibility (in CER), the MOS, and discrimination ABX scores compared to the official ZeroSpeech 2019 baseline or even the topline.

Includes members:
  Member "the zero resource speech challenge 2019 tts without t":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning robust and multilingual speech representations")
  Member "unsupervised speech representation learning using wavenet autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from abstract items to latent spaces to observed data and back compositional variational auto encoder")
  Member "machine speech chain with one shot speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning")
  Member "end to end feedback loss in speech chain framework via straight through estimator":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "listening while speaking speech chain by deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "the zero resource speech challenge 2017":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning robust and multilingual speech representations")
  Member "statistical parametric speech synthesis incorporating generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel framework for selection of gans for an application")
  Member "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "many to many voice conversion using conditional cycle consistent adversarial networks")

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)