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 "a comparative study on transformer vs rnn in speech applications"):
  Abstract: Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.

Includes members:
  Member "fastspeech fast robust and controllable text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised neural architecture search")
  Member "rwth asr systems for librispeech hybrid vs attention w o data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "language modeling with deep transformers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning robust and multilingual speech representations")
  Member "scaling neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "training tips for the transformer model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "neural speech synthesis with transformer network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi supervised neural architecture search")

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)