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 "dialog context aware end to end speech recognition"):
  Abstract: Existing speech recognition systems are typically built at the sentence level, although it is known that dialog context, e.g. higher-level knowledge that spans across sentences or speakers, can help the processing of long conversations. The recent progress in end-to-end speech recognition systems promises to integrate all available information (e.g. acoustic, language resources) into a single model, which is then jointly optimized. It seems natural that such dialog context information should thus also be integrated into the end-to-end models to improve further recognition accuracy. In this work, we present a dialog-context aware speech recognition model, which explicitly uses context information beyond sentence-level information, in an end-to-end fashion. Our dialog-context model captures a history of sentence-level context so that the whole system can be trained with dialog-context information in an end-to-end manner. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a comparable sentence-level end-to-end speech recognition system.

Includes members:
  Member "espnet end to end speech processing toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "building competitive direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "comparison of decoding strategies for ctc acoustic models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who needs words lexicon free speech recognition")
  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-4 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "dialog context language modeling with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "acoustic to word models with conversational context information")
  Member "very deep convolutional networks for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "joint ctc attention based end to end speech recognition using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "the microsoft 2016 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")

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)