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 "quality expectations of machine translation"):
  Abstract: Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself.

Includes members:
  Member "achieving open vocabulary neural machine translation with hybrid word character models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "authorship attribution in bangla literature using character level cnn")
  Member "neural versus phrase based machine translation quality a case study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "edinburgh neural machine translation systems for wmt 16":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "a character level decoder without explicit segmentation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "improving neural machine translation models with monolingual data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a brief survey of multilingual neural machine 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)