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 "speech to speech translation between untranscribed unknown languages"):
  Abstract: In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-to-sequence model that directly maps the source language speech to the target language's discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.

Includes members:
  Member "vqvae unsupervised unit discovery and multi scale code2spec inverter for zerospeech challenge 2019":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "the zero resource speech challenge 2019 tts without t":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning robust and multilingual speech representations")
  Member "direct speech to speech translation with a sequence to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning coupled policies for simultaneous machine translation")
  Member "multi scale alignment and contextual history for attention mechanism in sequence to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning")
  Member "machine speech chain with one shot speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "sequence to sequence models can directly translate foreign speech":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "covost a diverse multilingual speech to text translation corpus")

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)