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: "synthetic data for neural machine translation of spoken dialects"
  Abstract: In this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach transforms a given parallel corpus between a written language and a target language to a parallel corpus between a spoken dialect variant and the target language. Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is closely related to the source language. #R##N#The proposed approach is based on local embedding projection of distributed representations which utilizes monolingual embeddings to transform parallel data across language variants. We report experimental results on Levantine to English translation using Neural Machine Translation. We show that the generated data can improve a very large scale system by more than 2.8 Bleu points using synthetic spoken data which shows that it can be used to provide a reliable translation system for a spoken dialect that does not have sufficient parallel data.

Connected through hyperedge HE-1 (induced by source paper "generalized data augmentation for low resource translation"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "choosing transfer languages for cross lingual learning"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "style transfer as unsupervised machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "unsupervised cross lingual information retrieval using monolingual data only"

Connected through hyperedge HE-2 (induced by source paper "efficient autotuning of hyperparameters in approximate nearest neighbor search"):
  with members:
    - "billion scale similarity search with gpus"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "approximate principal direction trees"

Connected through hyperedge HE-3 (induced by source paper "neural machine translation into language varieties"):
  with members:
    - "a neural approach to language variety translation"
    - "attention is all you need"
    - "transfer learning across low resource related languages for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "discriminating similar languages evaluations and explorations"
    - "transfer learning for low resource neural machine 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)