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: "towards an arabic english machine translation based on semantic web"
  Abstract: Communication tools make the world like a small village and as a consequence people can contact with others who are from different societies or who speak different languages. This communication cannot happen effectively without Machine Translation because they can be found anytime and everywhere. There are a number of studies that have developed Machine Translation for the English language with so many other languages except the Arabic it has not been considered yet. Therefore we aim to highlight a roadmap for our proposed translation machine to provide an enhanced Arabic English translation based on Semantic.

Connected through hyperedge HE-1 (induced by source paper "extending a model for ontology based arabic english machine translation nan"):
  with members:
    - "machine translation using semantic web technologies a survey"
    - "gender aware spoken language translation applied to english arabic"
    - "machine translation history and evolution survey for arabic english translations"
    - "machine translation evaluation a survey"
    - "neural based machine translation for medical text domain based on european medicines agency leaflet texts"
    - "using ontology based context in the portuguese english translation of homographs in textual dialogues"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "machine translation using semantic web technologies a survey"):
  with members:
    - "lidioms a multilingual linked idioms data set"
    - "mag a multilingual knowledge base agnostic and deterministic entity linking approach"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "six challenges for neural machine translation"
    - "a survey of word reordering in statistical machine translation computational models and language phenomena"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "word2vec vs dbnary augmenting meteor using vector representations or lexical resources"

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)