human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "combining smt and nmt back translated data for efficient nmt"
  Abstract: Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation (Sennrich et al., 2016), which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model. #R##N#Generally, NMT models are used for back-translation. In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches. In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both. The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches.

Connected through hyperedge HE-1 (induced by source paper "evaluating low resource machine translation between chinese and vietnamese with back translation"):
  with members:
    - "a survey of methods to leverage monolingual data in low resource neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "simple fusion return of the language model"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "building a neural machine translation system using only synthetic parallel data"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-2 (induced by source paper "selecting artificially generated sentences for fine tuning neural machine translation"):
  with members:
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "transductive data selection algorithms for fine tuning neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "the adapt system description for the iwslt 2018 basque to english translation task"
    - "data selection with feature decay algorithms using an approximated target side"
    - "dynamic data selection for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "one sentence one model for neural machine translation"

Question: Please tell me which class the center node 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)