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 "libri light a benchmark for asr with limited or no supervision"):
  Abstract: We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books from the LibriVox project. It contains over 60K hours of audio, which is, to our knowledge, the largest freely-available corpus of speech. The audio has been segmented using voice activity detection and is tagged with SNR, speaker ID and genre descriptions. Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER). Settings (2) and (3) use limited textual resources (10 minutes to 10 hours) aligned with the speech. Setting (3) uses large amounts of unaligned text. They are evaluated on the standard LibriSpeech dev and test sets for comparison with the supervised state-of-the-art.

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 "self training for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning robust and multilingual speech representations")
  Member "vq wav2vec self supervised learning of discrete speech representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning robust and multilingual speech representations")
  Member "wav2vec unsupervised pre training for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning robust and multilingual speech representations")
  Member "unsupervised cross modal alignment of speech and text embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "representation learning for electronic health records")
  Member "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wav2vec unsupervised pre training for speech recognition")
  Member "speech2vec a sequence to sequence framework for learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")

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)