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 "a data efficient end to end spoken language understanding architecture"):
  Abstract: End-to-end architectures have been recently proposed for spoken language understanding (SLU) and semantic parsing. Based on a large amount of data, those models learn jointly acoustic and linguistic-sequential features. Such architectures give very good results in the context of domain, intent and slot detection, their application in a more complex semantic chunking and tagging task is less easy. For that, in many cases, models are combined with an external language model to enhance their performance. #R##N#In this paper we introduce a data efficient system which is trained end-to-end, with no additional, pre-trained external module. One key feature of our approach is an incremental training procedure where acoustic, language and semantic models are trained sequentially one after the other. The proposed model has a reasonable size and achieves competitive results with respect to state-of-the-art while using a small training dataset. In particular, we reach 24.02% Concept Error Rate (CER) on MEDIA/test while training on MEDIA/train without any additional data.

Includes members:
  Member "hybrid neural models for sequence modelling the best of three worlds":
    also connected through other hyperedges:
      (none)
  Member "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems")
  Member "towards end to end spoken language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems")
  Member "on the practical computational power of finite precision rnns for language recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "sequence to sequence models can directly translate foreign speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "asr error management for improving spoken language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability")
  Member "listen and translate a proof of concept for end to end speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual end to end speech translation")

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)