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 "neural machine translation training in a multi domain scenario"):
  Abstract: In this paper, we explore alternative ways to train a neural machine translation system in a multi-domain scenario. We investigate data concatenation (with fine tuning), model stacking (multi-level fine tuning), data selection and weighted ensemble. We evaluate these methods based on three criteria: i) translation quality, ii) training time, and iii) robustness towards out-of-domain tests. Our findings on Arabic-English and German-English language pairs show that the best translation quality can be achieved by building an initial system on a concatenation of available out-of-domain data and then fine-tuning it on in-domain data. Model stacking works best when training begins with the furthest out-of-domain data and the model is incrementally fine-tuned with the next furthest domain and so on. Data selection did not give the best results, but can be considered as a decent compromise between training time and translation quality. A weighted ensemble of different individual models performed better than data selection. It is beneficial in a scenario when there is no time for fine-tuning.

Includes members:
  Member "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pointer based fusion of bilingual lexicons into neural machine translation")
  Member "an empirical comparison of simple domain adaptation methods for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of multilingual neural machine translation")
  Member "dynamic data selection for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing data usage via differentiable rewards")
  Member "domain control for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptation for neural machine translation with iterative back translation")
  Member "fast domain adaptation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised domain adaptation for neural machine translation with iterative back translation")
  Member "domain specialization a post training domain adaptation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploring benefits of transfer learning in neural machine translation")
  Member "an arabic hebrew parallel corpus of ted talks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what do neural machine translation models learn about morphology")
  Member "edinburgh neural machine translation systems for wmt 16":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual denoising pre training for 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)