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 "towards language universal end to end speech recognition"):
  Abstract: Building speech recognizers in multiple languages typically involves replicating a monolingual training recipe for each language, or utilizing a multi-task learning approach where models for different languages have separate output labels but share some internal parameters. In this work, we exploit recent progress in end-to-end speech recognition to create a single multilingual speech recognition system capable of recognizing any of the languages seen in training. To do so, we propose the use of a universal character set that is shared among all languages. We also create a language-specific gating mechanism within the network that can modulate the network's internal representations in a language-specific way. We evaluate our proposed approach on the Microsoft Cortana task across three languages and show that our system outperforms both the individual monolingual systems and systems built with a multi-task learning approach. We also show that this model can be used to initialize a monolingual speech recognizer, and can be used to create a bilingual model for use in code-switching scenarios.

Includes members:
  Member "transfer learning for speech recognition on a budget":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient adjusted neuron activation profiles for comprehensive introspection of convolutional speech recognition models")
  Member "joint ctc attention based end to end speech recognition using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "advances in all neural speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for audio signal processing")
  Member "listen attend and spell":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "transfer learning for speech and language processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks")
  Member "fast and accurate recurrent neural network acoustic models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "eesen end to end speech recognition using deep rnn models and wfst based decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards zero shot learning for automatic phonemic transcription")
  Member "deep speech scaling up end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")

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)