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 "improving transformer based speech recognition using unsupervised pre training"):
  Abstract: Speech recognition technologies are gaining enormous popularity in various industrial applications. However, building a good speech recognition system usually requires large amounts of transcribed data, which is expensive to collect. To tackle this problem, an unsupervised pre-training method called Masked Predictive Coding is proposed, which can be applied for unsupervised pre-training with Transformer based model. Experiments on HKUST show that using the same training data, we can achieve CER 23.3%, exceeding the best end-to-end model by over 0.2% absolute CER. With more pre-training data, we can further reduce the CER to 21.0%, or a 11.8% relative CER reduction over baseline.

Includes members:
  Member "pre trained language model representations for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "a comparative study on transformer vs rnn in speech applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "an unsupervised autoregressive model for speech representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "learning problem agnostic speech representations from multiple self supervised tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "wav2vec unsupervised pre training for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning robust and multilingual speech representations")
  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-7 (induced by source paper "cif continuous integrate and fire for end to end speech recognition")
  Member "roberta a robustly optimized bert pretraining approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)