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 "an online attention based model for speech recognition"):
  Abstract: Attention-based end-to-end models such as Listen, Attend and Spell (LAS), simplify the whole pipeline of traditional automatic speech recognition (ASR) systems and become popular in the field of speech recognition. In previous work, researchers have shown that such architectures can acquire comparable results to state-of-the-art ASR systems, especially when using a bidirectional encoder and global soft attention (GSA) mechanism. However, bidirectional encoder and GSA are two obstacles for real-time speech recognition. In this work, we aim to stream LAS baseline by removing the above two obstacles. On the encoder side, we use a latency-controlled (LC) bidirectional structure to reduce the delay of forward computation. Meanwhile, an adaptive monotonic chunk-wise attention (AMoChA) mechanism is proposed to replace GSA for the calculation of attention weight distribution. Furthermore, we propose two methods to alleviate the huge performance degradation when combining LC and AMoChA. Finally, we successfully acquire an online LAS model, LC-AMoChA, which has only 3.5% relative performance reduction to LAS baseline on our internal Mandarin corpus.

Includes members:
  Member "exploring architectures data and units for streaming end to end speech recognition with rnn transducer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "extending recurrent neural aligner for streaming end to end speech recognition in mandarin":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cif continuous integrate and fire for end to end speech recognition")
  Member "online and linear time attention by enforcing monotonic alignments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition")
  Member "monotonic chunkwise attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient content based sparse attention with routing transformers")
  Member "learning hard alignments with variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "select and attend towards controllable content selection in text generation")
  Member "exploring neural transducers for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation")
  Member "improving the performance of online neural transducer models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "streaming automatic speech recognition with the transformer model")
  Member "local monotonic attention mechanism for end to end speech and language processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cif continuous integrate and fire for end to end speech recognition")

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)