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 corpus of ainu folklore and end to end speech recognition for ainu language"):
  Abstract: Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan. It is recognized as critically endangered by UNESCO and archiving and documentation of its language heritage is of paramount importance. Although a considerable amount of voice recordings of Ainu folklore has been produced and accumulated to save their culture, only a quite limited parts of them are transcribed so far. Thus, we started a project of automatic speech recognition (ASR) for the Ainu language in order to contribute to the development of annotated language archives. In this paper, we report speech corpus development and the structure and performance of end-to-end ASR for Ainu. We investigated four modeling units (phone, syllable, word piece, and word) and found that the syllable-based model performed best in terms of both word and phone recognition accuracy, which were about 60% and over 85% respectively in speaker-open condition. Furthermore, word and phone accuracy of 80% and 90% has been achieved in a speaker-closed setting. We also found out that a multilingual ASR training with additional speech corpora of English and Japanese further improves the speaker-open test accuracy.

Includes members:
  Member "how does learning rate decay help modern neural networks":
    also connected through other hyperedges:
      (none)
  Member "rwth asr systems for librispeech hybrid vs attention w o data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving uyghur asr systems with decoders using morpheme based language models")
  Member "state of the art speech recognition using multi stream self attention with dilated 1d convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures")
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "tied multitask learning for neural speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context aware self attention networks")
  Member "advancing acoustic to word ctc model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "subword regularization improving neural network translation models with multiple subword candidates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fastai a layered api for deep learning")

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)