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: "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
  Abstract: Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. Recently, Wang et al. (2018) proposed a novel reconstruction-based approach to alleviating dropped pronoun (DP) translation problems for neural machine translation models. In this work, we improve the original model from two perspectives. First, we employ a shared reconstructor to better exploit encoder and decoder representations. Second, we jointly learn to translate and predict DPs in an end-to-end manner, to avoid the errors propagated from an external DP prediction model. Experimental results show that our approach significantly improves both translation performance and DP prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "learning to reuse translations guiding neural machine translation with examples"):
  with members:
    - "exploiting deep representations for neural machine translation"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "tied multitask learning for neural speech translation"
    - "improving the transformer translation model with document level context"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "fusing recency into neural machine translation with an inter sentence gate model"
    - "deep contextualized word representations"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "one model to learn both zero pronoun prediction and translation"):
  with members:
    - "translating pro drop languages with reconstruction models"
    - "context aware neural machine translation learns anaphora resolution"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "neural machine translation with reconstruction"

Connected through hyperedge HE-4 (induced by source paper "bi directional differentiable input reconstruction for low resource neural machine translation"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "translating pro drop languages with reconstruction models"
    - "a call for clarity in reporting bleu scores"
    - "joint training for neural machine translation models with monolingual data"
    - "bi directional neural machine translation with synthetic parallel data"
    - "understanding back translation at scale"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-5 (induced by source paper "learning to refine source representations for neural machine translation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "attention is all you need"
    - "a gru gated attention model for neural machine translation"
    - "adversarial learning for neural dialogue generation"
    - "convolutional sequence to sequence learning"
    - "reading wikipedia to answer open domain questions"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"

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)