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: "data selection with feature decay algorithms using an approximated target side"
  Abstract: Data selection techniques applied to neural machine translation (NMT) aim to increase the performance of a model by retrieving a subset of sentences for use as training data. #R##N#One of the possible data selection techniques are transductive learning methods, which select the data based on the test set, i.e. the document to be translated. A limitation of these methods to date is that using the source-side test set does not by itself guarantee that sentences are selected with correct translations, or translations that are suitable given the test-set domain. Some corpora, such as subtitle corpora, may contain parallel sentences with inaccurate translations caused by localization or length restrictions. #R##N#In order to try to fix this problem, in this paper we propose to use an approximated target-side in addition to the source-side when selecting suitable sentence-pairs for training a model. This approximated target-side is built by pre-translating the source-side. #R##N#In this work, we explore the performance of this general idea for one specific data selection approach called Feature Decay Algorithms (FDA). #R##N#We train German-English NMT models on data selected by using the test set (source), the approximated target side, and a mixture of both. Our findings reveal that models built using a combination of outputs of FDA (using the test set and an approximated target side) perform better than those solely using the test set. We obtain a statistically significant improvement of more than 1.5 BLEU points over a model trained with all data, and more than 0.5 BLEU points over a strong FDA baseline that uses source-side information only.

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"
    - "the adapt system description for the iwslt 2018 basque to english translation task"
    - "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"
    - "the adapt system description for the iwslt 2018 basque to english translation task"
    - "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"
    - "the adapt system description for the iwslt 2018 basque to english translation task"
    - "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)