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 "word level speech recognition with a dynamic lexicon"):
  Abstract: We propose a direct-to-word sequence model with a dynamic lexicon. Our word network constructs word embeddings dynamically from the character level tokens. The word network can be integrated seamlessly with arbitrary sequence models including Connectionist Temporal Classification and encoder-decoder models with attention. Sub-word units are commonly used in speech recognition yet are generated without the use of acoustic context. We show our direct-to-word model can achieve word error rate gains over sub-word level models for speech recognition. Furthermore, we empirically validate that the word-level embeddings we learn contain significant acoustic information, making them more suitable for use in speech recognition. We also show that our direct-to-word approach retains the ability to predict words not seen at training time without any retraining.

Includes members:
  Member "sequence to sequence speech recognition with time depth separable convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "espresso a fast end to end neural speech recognition toolkit")
  Member "who needs words lexicon free speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "espresso a fast end to end neural speech recognition toolkit")
  Member "a fully differentiable beam search decoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised speech recognition via local prior matching")
  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 "advancing acoustic to word ctc model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "wav2letter the fastest open source speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions")
  Member "improved training of end to end attention models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling up online speech recognition using convnets")
  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 "a fully differentiable beam search decoder")

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)