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: "building a neural machine translation system using only synthetic parallel data"
  Abstract: Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this study, we build NMT systems using only synthetic parallel data. As an efficient alternative to real parallel data, we also present a new type of synthetic parallel corpus. The proposed pseudo parallel data are distinct from previous works in that ground truth and synthetic examples are mixed on both sides of sentence pairs. Experiments on Czech-German and French-German translations demonstrate the efficacy of the proposed pseudo parallel corpus, which shows not only enhanced results for bidirectional translation tasks but also substantial improvement with the aid of a ground truth real parallel corpus.

Connected through hyperedge HE-1 (induced by source paper "evaluating low resource machine translation between chinese and vietnamese with back translation"):
  with members:
    - "combining smt and nmt back translated data for efficient nmt"
    - "a survey of methods to leverage monolingual data in low resource neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "simple fusion return of the language model"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-2 (induced by source paper "using monolingual data in neural machine translation a systematic study"):
  with members:
    - "investigating backtranslation in neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "neural machine translation from simplified translations"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-3 (induced by source paper "a survey of domain adaptation for neural machine translation"):
  with members:
    - "domain generalization via conditional invariant representation"
    - "multi adversarial domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "attention is all you need"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "adversarial discriminative domain adaptation"
    - "multiple source domain adaptation with adversarial training of neural networks"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "investigating backtranslation in neural machine translation"):
  with members:
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "synthetic and natural noise both break neural machine translation"
    - "six challenges for neural machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural versus phrase based machine translation quality a case study"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "domain adaptation using adversarial learning for autonomous navigation"):
  with members:
    - "group normalization"
    - "reinforcement learning based recommender system using biclustering technique"
    - "driving policy transfer via modularity and abstraction"
    - "polyphonic music generation with sequence generative adversarial networks"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "virtual to real reinforcement learning for autonomous driving"
    - "domain adaptation for visual applications a comprehensive survey"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"

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)