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: "improving character level japanese chinese neural machine translation with radicals as an additional input feature"
  Abstract: In recent years, Neural Machine Translation (NMT) has been proven to get impressive results. While some additional linguistic features of input words improve word-level NMT, any additional character features have not been used to improve character-level NMT so far. In this paper, we show that the radicals of Chinese characters (or kanji), as a character feature information, can be easily provide further improvements in the character-level NMT. In experiments on WAT2016 Japanese-Chinese scientific paper excerpt corpus (ASPEC-JP), we find that the proposed method improves the translation quality according to two aspects: perplexity and BLEU. The character-level NMT with the radical input feature's model got a state-of-the-art result of 40.61 BLEU points in the test set, which is an improvement of about 8.6 BLEU points over the best system on the WAT2016 Japanese-to-Chinese translation subtask with ASPEC-JP. The improvements over the character-level NMT with no additional input feature are up to about 1.5 and 1.4 BLEU points in the development-test set and the test set of the corpus, respectively.

Connected through hyperedge HE-1 (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"
    - "combining advanced methods in japanese vietnamese neural machine translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "apply chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "imagination improves multimodal translation"
    - "word subword or character an empirical study of granularity in chinese english nmt"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "multilingual multi modal embeddings for natural language processing"
    - "towards string to tree neural machine translation"
    - "learning character level compositionality with visual features"
    - "six challenges for neural machine translation"
    - "context gates for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "improving the robustness of speech translation"):
  with members:
    - "towards robust neural machine translation"
    - "towards end to end spoken language understanding"
    - "end to end automatic speech translation of audiobooks"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "synthetic and natural noise both break neural machine translation"
    - "disentangling asr and mt errors in speech translation"
    - "six challenges 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)