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 "learning hierarchical discrete linguistic units from visually grounded speech"):
  Abstract: In this paper, we present a method for learning discrete linguistic units by incorporating vector quantization layers into neural models of visually grounded speech. We show that our method is capable of capturing both word-level and sub-word units, depending on how it is configured. What differentiates this paper from prior work on speech unit learning is the choice of training objective. Rather than using a reconstruction-based loss, we use a discriminative, multimodal grounding objective which forces the learned units to be useful for semantic image retrieval. We evaluate the sub-word units on the ZeroSpeech 2019 challenge, achieving a 27.3\% reduction in ABX error rate over the top-performing submission, while keeping the bitrate approximately the same. We also present experiments demonstrating the noise robustness of these units. Finally, we show that a model with multiple quantizers can simultaneously learn phone-like detectors at a lower layer and word-like detectors at a higher layer. We show that these detectors are highly accurate, discovering 279 words with an F1 score of greater than 0.5.

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 "vq wav2vec self supervised learning of discrete speech representations")
  Member "models of visually grounded speech signal pay attention to nouns a bilingual experiment on english and japanese":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "word recognition competition and activation in a model of visually grounded speech")
  Member "semantic speech retrieval with a visually grounded model of untranscribed speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning from audio visual grounding to speech recognition")
  Member "unsupervised end to end learning of discrete linguistic units for voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speech to speech translation between untranscribed unknown languages")
  Member "language learning using speech to image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "word recognition competition and activation in a model of visually grounded speech")
  Member "towards visually grounded sub word speech unit discovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "large scale representation learning from visually grounded untranscribed speech")
  Member "the zero resource speech challenge 2019 tts without t":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning robust and multilingual speech representations")
  Member "word recognition competition and activation in a model of visually grounded speech":
    also connected through other hyperedges:
      (none)

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)