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 "on the choice of modeling unit for sequence to sequence speech recognition"):
  Abstract: In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various target units (phoneme, grapheme, and word-piece); across all tasks, we find that grapheme or word-piece models consistently outperform phoneme-based models, even though they are evaluated without a lexicon or an external language model. We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model. Rescoring an N-best list generated by the phonemic system, however, provides limited improvements. Further analysis shows that the word-piece-based models produce more diverse N-best hypotheses, and thus lower oracle WERs, than phonemic models.

Includes members:
  Member "lingvo a modular and scalable framework for sequence to sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "the capio 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions")
  Member "exploring architectures data and units for streaming end to end speech recognition with rnn transducer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "improved training of end to end attention models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling up online speech recognition using convnets")
  Member "optimal completion distillation for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach")
  Member "sequence to sequence models can directly translate foreign speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "state of the art speech recognition with sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "minimum word error rate training for attention based sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "word level speech recognition with a dynamic lexicon")

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)