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: "translating pro drop languages with reconstruction models"
  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. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese-English and Japanese-English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs.

Connected through hyperedge HE-1 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual neural machine translation with knowledge distillation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "massively multilingual adversarial speech recognition"
    - "a survey of domain adaptation for neural machine translation"
    - "domain adversarial training for accented speech recognition"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "one model to learn both zero pronoun prediction and translation"):
  with members:
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "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-3 (induced by source paper "recovering dropped pronouns in chinese conversations via modeling their referents"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "structured attention networks"
    - "hierarchical recurrent attention network for response generation"
    - "a novel approach to dropped pronoun translation"
    - "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-4 (induced by source paper "bi directional differentiable input reconstruction for low resource neural machine translation"):
  with members:
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "universal neural machine translation for extremely low resource languages"
    - "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 "document level neural machine translation with hierarchical attention networks"):
  with members:
    - "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"
    - "opennmt open source toolkit for neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "document context neural machine translation with memory networks"

Connected through hyperedge HE-6 (induced by source paper "chinese portuguese machine translation a study on building parallel corpora from comparable texts"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "attention is all you need"
    - "exploiting cross sentence context for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"):
  with members:
    - "tied multitask learning for neural speech translation"
    - "exploiting cross sentence context for neural machine translation"
    - "neural machine translation with reconstruction"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "multi task sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "domain adaptation for statistical machine translation"):
  with members:
    - "exploiting cross sentence context for neural machine translation"
    - "a novel approach to dropped pronoun translation"
    - "automatic construction of discourse corpora for dialogue 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)