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: "adaptation of machine translation models with back translated data using transductive data selection methods"
  Abstract: Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? #R##N#In this work we use Infrequent N-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. #R##N#Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding n-grams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach.

Connected through hyperedge HE-1 (induced by source paper "tag less back translation"):
  with members:
    - "tagged back translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "iterative batch back translation for neural machine translation a conceptual model"):
  with members:
    - "tagged back translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "scaling neural machine translation"
    - "joint training for neural machine translation models with monolingual data"

Connected through hyperedge HE-3 (induced by source paper "selecting artificially generated sentences for fine tuning neural machine translation"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "transductive data selection algorithms for fine tuning neural machine translation"):
  with members:
    - "a survey of domain adaptation for 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"
    - "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-5 (induced by source paper "combining smt and nmt back translated data for efficient nmt"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "understanding back translation at scale"
    - "opennmt open source toolkit for neural machine translation"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "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)