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: "a novel approach to dropped pronoun translation"
  Abstract: Dropped Pronouns (DP) in which pronouns are frequently dropped in the source language but should be retained in the target language are challenge in machine translation. In response to this problem, we propose a semi-supervised approach to recall possibly missing pronouns in the translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation is two-phase: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. Experimental results show that our approach achieves a significant improvement of 1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy.

Connected through hyperedge HE-1 (induced by source paper "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"):
  with members:
    - "modeling coherence for discourse neural machine translation"
    - "improving the transformer translation model with document level context"
    - "training tips for the transformer model"
    - "context aware neural machine translation learns anaphora resolution"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "neural machine translation with extended context"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "recovering dropped pronouns in chinese conversations via modeling their referents"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "translating pro drop languages with reconstruction models"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "structured attention networks"
    - "hierarchical recurrent attention network for response generation"
    - "tracking the world state with recurrent entity networks"
    - "key value memory networks for directly reading documents"
    - "end to end memory networks"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation for statistical machine translation"):
  with members:
    - "translating pro drop languages with reconstruction models"
    - "exploiting cross sentence context for neural machine translation"
    - "automatic construction of discourse corpora for dialogue translation"

Connected through hyperedge HE-4 (induced by source paper "neural recovery machine for chinese dropped pronoun"):
  with members:
    - "a deep neural network for chinese zero pronoun resolution"
    - "distributed representations of words and phrases and their compositionality"

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)