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 "what do neural machine translation models learn about morphology"):
  Abstract: Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.

Includes members:
  Member "a hybrid morpheme word representation for machine translation of morphologically rich languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pointer based fusion of bilingual lexicons into neural machine translation")
  Member "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "fine grained analysis of sentence embeddings using auxiliary prediction tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "representation of linguistic form and function in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "word representation models for morphologically rich languages in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "achieving open vocabulary neural machine translation with hybrid word character models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "authorship attribution in bangla literature using character level cnn")

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)