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: "context aware learning for neural machine translation"
  Abstract: Interest in larger-context neural machine translation, including document-level and multi-modal translation, has been growing. Multiple works have proposed new network architectures or evaluation schemes, but potentially helpful context is still sometimes ignored by larger-context translation models. In this paper, we propose a novel learning algorithm that explicitly encourages a neural translation model to take into account additional context using a multilevel pair-wise ranking loss. We evaluate the proposed learning algorithm with a transformer-based larger-context translation system on document-level translation. By comparing performance using actual and random contexts, we show that a model trained with the proposed algorithm is more sensitive to the additional context.

Connected through hyperedge HE-1 (induced by source paper "a survey on document level machine translation methods and evaluation"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "findings of the third workshop on neural generation and translation"
    - "naver labs europe s systems for the document level generation and translation task at wngt 2019"
    - "improving conditioning in context aware sequence to sequence models"
    - "evaluating pronominal anaphora in machine translation an evaluation measure and a test suite"
    - "using whole document context in neural machine translation"
    - "english czech systems in wmt19 document level transformer"
    - "when and why is document level context useful in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "analysing coreference in transformer outputs"):
  with members:
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "document level neural machine translation with hierarchical attention networks"
    - "context aware neural machine translation learns anaphora resolution"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in 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)