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: "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
  Abstract: Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model. This is particularly inconvenient for language pairs for which enough parallel text is not available. In this paper, we use monolingual linguistic resources in the source side to address this challenging problem based on a multi-task learning approach. More specifically, we scaffold the machine translation task on auxiliary tasks including semantic parsing, syntactic parsing, and named-entity recognition. This effectively injects semantic and/or syntactic knowledge into the translation model, which would otherwise require a large amount of training bitext. We empirically evaluate and show the effectiveness of our multi-task learning approach on three translation tasks: English-to-French, English-to-Farsi, and English-to-Vietnamese.

Connected through hyperedge HE-1 (induced by source paper "learning to multi task learn for better neural machine translation"):
  with members:
    - "autosem automatic task selection and mixing in multi task learning"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "learning to reweight examples for robust deep learning"
    - "scheduled multi task learning from syntax to translation"
    - "learning to multitask"
    - "opennmt open source toolkit for neural machine translation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"

Connected through hyperedge HE-2 (induced by source paper "making neural machine reading comprehension faster"):
  with members:
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "a fully attention based information retriever"
    - "lightweight convolutional approaches to reading comprehension on squad"
    - "phase conductor on multi layered attentions for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "learning to compute word embeddings on the fly"
    - "contextualized word representations for reading comprehension"
    - "smarnet teaching machines to read and comprehend like human"

Connected through hyperedge HE-3 (induced by source paper "promoting the knowledge of source syntax in transformer nmt is not needed"):
  with members:
    - "training tips for the transformer model"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "scheduled multi task learning from syntax to translation"
    - "linguistically informed self attention for semantic role labeling"
    - "learning to parse and translate improves neural machine translation"
    - "predicting target language ccg supertags improves neural machine translation"
    - "attention is all you need"
    - "modeling target side inflection in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "multi task deep morphological analyzer context aware joint morphological tagging and lemma prediction"):
  with members:
    - "meta multi task learning for sequence modeling"
    - "scheduled multi task learning from syntax to translation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "cross lingual character level neural morphological tagging"
    - "improving generalization performance by switching from adam to sgd"
    - "recent trends in deep learning based natural language processing"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "parameter sharing methods for multilingual self attentional translation models"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "regularizing neural networks by penalizing confident output distributions"
    - "what do neural machine translation models learn about morphology"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "tying word vectors and word classifiers a loss framework for language modeling"

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)