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: "multimodal machine translation with embedding prediction"
  Abstract: Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.

Connected through hyperedge HE-1 (induced by source paper "debiasing word embeddings improves multimodal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "all but the top simple and effective postprocessing for word representations"

Connected through hyperedge HE-2 (induced by source paper "dynamic fusion attentional language model for neural machine translation"):
  with members:
    - "simple fusion return of the language model"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "cold fusion training seq2seq models together with language models"
    - "unsupervised pretraining for sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "linguistic input features improve neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based 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)