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: "first result on arabic neural machine translation"
  Abstract: Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation. We notice however that much of research on neural machine translation has focused on European languages despite its language agnostic nature. In this paper, we apply neural machine translation to the task of Arabic translation (Ar En) and compare it against a standard phrase-based translation system. We run extensive comparison using various configurations in preprocessing Arabic script and show that the phrase-based and neural translation systems perform comparably to each other and that proper preprocessing of Arabic script has a similar effect on both of the systems. We however observe that the neural machine translation significantly outperform the phrase-based system on an out-of-domain test set, making it attractive for real-world deployment.

Connected through hyperedge HE-1 (induced by source paper "the impact of preprocessing on arabic english statistical and neural machine translation"):
  with members:
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "qcri machine translation systems for iwslt 16"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "amharic arabic neural machine translation"):
  with members:
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "a recipe for arabic english neural machine translation"):
  with members:
    - "marian fast neural machine translation in c"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"
    - "building subject aligned comparable corpora and mining it for truly parallel sentence pairs"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"):
  with members:
    - "qcri machine translation systems for iwslt 16"
    - "what do neural machine translation models learn about morphology"
    - "character based neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "character based neural machine translation"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "low resourced machine translation via morpho syntactic modeling the case of dialectal arabic"):
  with members:
    - "boosting neural machine translation"
    - "a large scale corpus of gulf arabic"
    - "morphological constraints for phrase pivot statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "large scale machine translation between arabic and hebrew available corpora and initial results"):
  with members:
    - "morphological constraints for phrase pivot statistical machine translation"
    - "an arabic hebrew parallel corpus of ted talks"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"
    - "on using very large target vocabulary 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)