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: "recursive neural network based preordering for english to japanese machine translation"
  Abstract: The word order between source and target languages significantly influences the translation quality in machine translation. Preordering can effectively address this problem. Previous preordering methods require a manual feature design, making language dependent design costly. In this paper, we propose a preordering method with a recursive neural network that learns features from raw inputs. Experiments show that the proposed method achieves comparable gain in translation quality to the state-of-the-art method but without a manual feature design.

Connected through hyperedge HE-1 (induced by source paper "guiding non autoregressive neural machine translation decoding with reordering information"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "syntactically supervised transformers for faster neural machine translation"
    - "retrieving sequential information for non autoregressive neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "addressing word order divergence in multilingual neural machine translation for extremely low resource languages"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "universal neural machine translation for extremely low resource languages"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "learning word vectors for 157 languages"
    - "neural cross lingual named entity recognition with minimal resources"
    - "transfer learning across low resource related languages for neural machine translation"
    - "fully character level neural machine translation without explicit segmentation"
    - "transfer learning for low resource 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)