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 "self training for end to end speech recognition"):
  Abstract: We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model by leveraging unlabelled data. Key to our approach are a strong baseline acoustic and language model used to generate the pseudo-labels, a robust and stable beam-search decoder, and a novel ensemble approach used to increase pseudo-label diversity. Experiments on the LibriSpeech corpus show that self-training with a single model can yield a 21% relative WER improvement on clean data over a baseline trained on 100 hours of labelled data. We also evaluate label filtering approaches to increase pseudo-label quality. With an ensemble of six models in conjunction with label filtering, self-training yields a 26% relative improvement and bridges 55.6% of the gap between the baseline and an oracle model trained with all of the labels.

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 "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 "lessons from building acoustic models with a million hours of speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving efficiency in large scale decentralized distributed training")
  Member "back translation style data augmentation for end to end asr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "cycle consistency training for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "adversarial training of end to end speech recognition using a criticizing language model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "fully convolutional 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)