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: "an arabic hebrew parallel corpus of ted talks"
  Abstract: We describe an Arabic-Hebrew parallel corpus of TED talks built upon WIT3, the Web inventory that repurposes the original content of the TED website in a way which is more convenient for MT researchers. The benchmark consists of about 2,000 talks, whose subtitles in Arabic and Hebrew have been accurately aligned and rearranged in sentences, for a total of about 3.5M tokens per language. Talks have been partitioned in train, development and test sets similarly in all respects to the MT tasks of the IWSLT 2016 evaluation campaign. In addition to describing the benchmark, we list the problems encountered in preparing it and the novel methods designed to solve them. Baseline MT results and some measures on sentence length are provided as an extrinsic evaluation of the quality of the benchmark.

Connected through hyperedge HE-1 (induced by source paper "what do neural machine translation models learn about morphology"):
  with members:
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "character based neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-2 (induced by source paper "neural machine translation training in a multi domain scenario"):
  with members:
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "domain specialization a post training domain adaptation for neural machine translation"
    - "edinburgh neural machine translation systems for wmt 16"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-3 (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"
    - "first result on arabic neural machine translation"
    - "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)