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 "lead2gold towards exploiting the full potential of noisy transcriptions for speech recognition"):
  Abstract: The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions. Based on a noise model of transcription errors, Lead2Gold searches for better transcriptions of the training data with a beam search that takes this noise model into account. The beam search is differentiable and does not require a forced alignment step, thus the whole system is trained end-to-end. Lead2Gold can be viewed as a new loss function that can be used on top of any sequence-to-sequence deep neural network. We conduct proof-of-concept experiments on noisy transcriptions generated from letter corruptions with different noise levels. We show that Lead2Gold obtains a better ASR accuracy than a competitive baseline which does not account for the (artificially-introduced) transcription noise.

Includes members:
  Member "active and semi supervised learning in asr benefits on the acoustic and language models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "consistency based semi supervised active learning towards minimizing labeling budget")
  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 "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 "wav2letter the fastest open source speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions")
  Member "label noise robust generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "letter based speech recognition with gated convnets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "improving lstm ctc based asr performance in domains with limited training data":
    also connected through other hyperedges:
      (none)
  Member "wav2letter an end to end convnet based speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning robust and multilingual speech representations")

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)