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 "cif continuous integrate and fire for end to end speech recognition"):
  Abstract: Automatic speech recognition (ASR) system is undergoing an exciting pathway to be more simplified and practical with the spring up of various end-to-end models. However, the mainstream of them neglects the positioning of token boundaries from continuous speech, which is considered crucial in human language learning and instant speech recognition. In this work, we propose Continuous Integrate-and-Fire (CIF), a 'soft' and 'monotonic' acoustic-to-linguistic alignment mechanism that addresses the boundary positioning by simulating the integrate-and-fire neuron model using continuous functions under the encoder-decoder framework. As the connection between the encoder and decoder, the CIF forwardly integrates the information in the encoded acoustic representations to determine a boundary and instantly fires the integrated information to the decoder once a boundary is located. Multiple effective strategies are introduced to the CIF-based model to alleviate the problems brought by the inaccurate positioning. Besides, multi-task learning is performed during training and an external language model is incorporated during inference to further boost the model performance. Evaluated on multiple ASR datasets that cover different languages and speech types, the CIF-based model shows stable convergence and competitive performance. Especially, it achieves a word error rate (WER) of 2.92% on the test-clean of Librispeech.

Includes members:
  Member "transformers with convolutional context for asr":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  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 "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving transformer based speech recognition using unsupervised pre training")
  Member "espnet end to end speech processing toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "wav2letter the fastest open source speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions")
  Member "an online attention based model for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "aishell 2 transforming mandarin asr research into industrial scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "speaker aware speech transformer")
  Member "improved training of end to end attention models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaling up online speech recognition using convnets")

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)