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 "domain translationese and noise in synthetic data for neural machine translation"):
  Abstract: The quality of neural machine translation can be improved by leveraging additional monolingual resources to create synthetic training data. Source-side monolingual data can be (forward-)translated into the target language for self-training; target-side monolingual data can be back-translated. It has been widely reported that back-translation delivers superior results, but could this be due to artefacts in the test sets? We perform a case study using French-English news translation task and separate test sets based on their original languages. We show that forward translation delivers superior gains in terms of BLEU on sentences that were originally in the source language, complementing previous studies which show large improvements with back-translation on sentences that were originally in the target language. To better understand when and why forward and back-translation are effective, we study the role of domains, translationese, and noise. While translationese effects are well known to influence MT evaluation, we also find evidence that news data from different languages shows subtle domain differences, which is another explanation for varying performance on different portions of the test set. We perform additional low-resource experiments which demonstrate that forward translation is more sensitive to the quality of the initial translation system than back-translation, and tends to perform worse in low-resource settings.

Includes members:
  Member "tagged back translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tag less back translation")
  Member "the effect of translationese in machine translation test sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the evaluation of machine translation systems trained with back translation")
  Member "using monolingual data in neural machine translation a systematic study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational template machine for data to text generation")
  Member "the source target domain mismatch problem in machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "facebook ai s wat19 myanmar english translation task submission")
  Member "ape at scale and its implications on mt evaluation biases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "context aware monolingual repair for neural machine translation")
  Member "on the evaluation of machine translation systems trained with back translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "question answering is a format when is it useful")
  Member "translationese in machine translation evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "marian fast neural machine translation in c":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "context in neural machine translation a review of models and evaluations")

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)