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 supervised speech recognition via local prior matching"):
  Abstract: For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by assigning them low probability. In this work, we propose local prior matching (LPM), a semi-supervised objective that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. We demonstrate that LPM is theoretically well-motivated, simple to implement, and superior to existing knowledge distillation techniques under comparable settings. Starting from a baseline trained on 100 hours of labeled speech, with an additional 360 hours of unlabeled data, LPM recovers 54% and 73% of the word error rate on clean and noisy test sets relative to a fully supervised model on the same data.

Includes members:
  Member "attentive adversarial learning for domain invariant training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "speaker adaptation for attention based end to end speech recognition")
  Member "parallel neural text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "waveflow a compact flow based model for raw audio")
  Member "libri light a benchmark for asr with limited or no supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning robust and multilingual speech representations")
  Member "lessons from building acoustic models with a million hours of speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving efficiency in large scale decentralized distributed training")
  Member "a fully differentiable beam search decoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures")
  Member "back translation style data augmentation for end to end asr":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self training for end to end speech recognition")
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "self training 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)