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 "sequence to sequence automatic speech recognition with word embedding regularization and fused decoding"):
  Abstract: In this paper, we investigate the benefit that off-the-shelf word embedding can bring to the sequence-to-sequence (seq-to-seq) automatic speech recognition (ASR). We first introduced the word embedding regularization by maximizing the cosine similarity between a transformed decoder feature and the target word embedding. Based on the regularized decoder, we further proposed the fused decoding mechanism. This allows the decoder to consider the semantic consistency during decoding by absorbing the information carried by the transformed decoder feature, which is learned to be close to the target word embedding. Initial results on LibriSpeech demonstrated that pre-trained word embedding can significantly lower ASR recognition error with a negligible cost, and the choice of word embedding algorithms among Skip-gram, CBOW and BERT is important.

Includes members:
  Member "rewe regressing word embeddings for regularization of neural machine translation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regressing word and sentence embeddings for regularization of neural machine translation")
  Member "back translation style data augmentation for end to end asr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "state of the art speech recognition with sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "listening while speaking speech chain by deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "towards better decoding and language model integration in sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking image mixture for unsupervised visual representation learning")

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)