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: "the adapt system description for the iwslt 2018 basque to english translation task"
  Abstract: In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data. #R##N#Accordingly, we used synthetic data to improve the translation quality produced by a model built using only authentic data. Our proposal uses back-translated data to: (a) create new sentences, so the system can be trained with more data; and (b) translate sentences that are close to the test set, so the model can be fine-tuned to the document to be translated.

Connected through hyperedge HE-1 (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"
    - "combining smt and nmt back translated data for efficient nmt"
    - "transductive data selection algorithms for fine tuning neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "transductive data selection algorithms for fine tuning neural machine translation"):
  with members:
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "a survey of domain adaptation for neural machine translation"
    - "data selection with feature decay algorithms using an approximated target side"
    - "opennmt open source toolkit for neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "one sentence one model for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "adaptation of machine translation models with back translated data using transductive data selection methods"):
  with members:
    - "investigating backtranslation in neural machine translation"
    - "data selection with feature decay algorithms using an approximated target side"
    - "opennmt open source toolkit for neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

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)