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: "qcri machine translation systems for iwslt 16"
  Abstract: This paper describes QCRI's machine translation systems for the IWSLT 2016 evaluation campaign. We participated in the Arabic->English and English->Arabic tracks. We built both Phrase-based and Neural machine translation models, in an effort to probe whether the newly emerged NMT framework surpasses the traditional phrase-based systems in Arabic-English language pairs. We trained a very strong phrase-based system including, a big language model, the Operation Sequence Model, Neural Network Joint Model and Class-based models along with different domain adaptation techniques such as MML filtering, mixture modeling and using fine tuning over NNJM model. However, a Neural MT system, trained by stacking data from different genres through fine-tuning, and applying ensemble over 8 models, beat our very strong phrase-based system by a significant 2 BLEU points margin in Arabic->English direction. We did not obtain similar gains in the other direction but were still able to outperform the phrase-based system. We also applied system combination on phrase-based and NMT outputs.

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"
    - "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"
    - "first result on arabic 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"

Connected through hyperedge HE-2 (induced by source paper "an operation sequence model for explainable neural machine translation"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "training tips for the transformer model"
    - "neural machine translation decoding with terminology constraints"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "unfolding and shrinking neural machine translation ensembles"):
  with members:
    - "ensemble distillation for neural machine translation"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "noiseout a simple way to prune neural networks"
    - "learning compact recurrent neural networks"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "learning both weights and connections for efficient neural networks"
    - "neural reranking improves subjective quality of machine translation naist at wat2015"

Connected through hyperedge HE-4 (induced by source paper "a challenge set approach to evaluating machine translation"):
  with members:
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural versus phrase based machine translation quality a case study"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"):
  with members:
    - "what do neural machine translation models learn about morphology"
    - "character based neural machine translation"
    - "first result on arabic 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"

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)