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: "syntax aware neural machine translation using ccg"
  Abstract: Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling source or target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask training? We introduce syntactic information in the form of CCG supertags either in the source as an extra feature in the embedding, or in the target, by interleaving the target supertags with the word sequence. Our results on WMT data show that explicitly modeling syntax improves machine translation quality for English-German, a high-resource pair, and for English-Romanian, a low-resource pair and also several syntactic phenomena including prepositional phrase attachment. Furthermore, a tight coupling of words and syntax improves translation quality more than multitask training.

Connected through hyperedge HE-1 (induced by source paper "scheduled multi task learning from syntax to translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "one model to learn them all"
    - "what do neural machine translation models learn about morphology"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-2 (induced by source paper "contextual sequence modeling for recommendation with recurrent neural networks"):
  with members:
    - "meta prod2vec product embeddings using side information for recommendation"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "general factorization framework for context aware recommendations"
    - "architectural complexity measures of recurrent neural networks"
    - "linguistic input features improve neural machine translation"
    - "on multiplicative integration with recurrent neural networks"
    - "session based recommendations with recurrent neural networks"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-3 (induced by source paper "modeling target side inflection in neural machine translation"):
  with members:
    - "linguistically motivated vocabulary reduction for neural machine translation from turkish to english"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

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)