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 "minimum bayes risk training of rnn transducer for end to end speech recognition"):
  Abstract: In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR training is conducted via minimizing the expected edit distance between the reference label sequence and on-the-fly generated N-best hypothesis. We also introduce a heuristic to incorporate an external neural network language model (NNLM) in RNN-T beam search decoding and explore MBR training with the external NNLM. Experimental results demonstrate an MBR trained model outperforms a RNN-T trained model substantially and further improvements can be achieved if trained with an external NNLM. Our best MBR trained system achieves absolute character error rate (CER) reductions of 1.2% and 0.5% on read and spontaneous Mandarin speech respectively over a strong convolution and transformer based RNN-T baseline trained on ~21,000 hours of speech.

Includes members:
  Member "improving rnn transducer modeling for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming automatic speech recognition with the transformer model")
  Member "transformer based acoustic modeling for hybrid speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling up online speech recognition using convnets")
  Member "streaming end to end speech recognition for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "exploring rnn transducer for chinese speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "exploring architectures data and units for streaming end to end speech recognition with rnn transducer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "building competitive direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "optimizing expected word error rate via sampling for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the inductive bias of word character level multi task learning for speech recognition")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)