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 "exploring neural transducers for end to end speech recognition"):
  Abstract: In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue - RNNTransducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models - when all encoder layers are forward only, and when encoders downsample the input representation aggressively.

Includes members:
  Member "online and linear time attention by enforcing monotonic alignments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition")
  Member "english conversational telephone speech recognition by humans and machines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition")
  Member "an online sequence to sequence model for noisy speech recognition":
    also connected through other hyperedges:
      (none)
  Member "wav2letter an end to end convnet based speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning robust and multilingual speech representations")
  Member "reducing bias in production speech models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust sound event detection in bioacoustic sensor networks")
  Member "towards better decoding and language model integration in sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking image mixture for unsupervised visual representation learning")
  Member "dense prediction on sequences with time dilated convolutions for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi representation knowledge distillation for audio classification")
  Member "segmental recurrent neural networks for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation")

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)