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: "combining advanced methods in japanese vietnamese neural machine translation"
  Abstract: Neural machine translation (NMT) systems have recently obtained state-of-the art in many machine translation systems between popular language pairs because of the availability of data. For low-resourced language pairs, there are few researches in this field due to the lack of bilingual data. In this paper, we attempt to build the first NMT systems for a low-resourced language pairs:Japanese-Vietnamese. We have also shown significant improvements when combining advanced methods to reduce the adverse impacts of data sparsity and improve the quality of NMT systems. In addition, we proposed a variant of Byte-Pair Encoding algorithm to perform effective word segmentation for Vietnamese texts and alleviate the rare-word problem that persists in NMT systems.

Connected through hyperedge HE-1 (induced by source paper "overcoming the rare word problem for low resource language pairs in neural machine translation"):
  with members:
    - "polylingual wordnet"
    - "extending and improving wordnet via unsupervised word embeddings"
    - "opennmt open source toolkit for neural machine translation"
    - "improving lexical choice in neural machine translation"
    - "six challenges for neural machine translation"
    - "edinburgh neural machine translation systems for wmt 16"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "how transformer revitalizes character based neural machine translation an investigation on japanese vietnamese translation systems"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "neural machine translation of logographic languages using sub character level information"
    - "improving character level japanese chinese neural machine translation with radicals as an additional input feature"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "fully character level neural machine translation without explicit segmentation"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"

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)