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: "toward a full scale neural machine translation in production the booking com use case"
  Abstract: While some remarkable progress has been made in neural machine translation (NMT) research, there have not been many reports on its development and evaluation in practice. This paper tries to fill this gap by presenting some of our findings from building an in-house travel domain NMT system in a large scale E-commerce setting. The three major topics that we cover are optimization and training (including different optimization strategies and corpus sizes), handling real-world content and evaluating results.

Connected through hyperedge HE-1 (induced by source paper "opennmt neural machine translation toolkit"):
  with members:
    - "structured attention networks"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "effective strategies in zero shot neural machine translation"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "challenges in data to document generation"

Connected through hyperedge HE-2 (induced by source paper "returnn as a generic flexible neural toolkit with application to translation and speech recognition"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "machine translation at booking com journey and lessons learned"
    - "english conversational telephone speech recognition by humans and machines"
    - "sockeye a toolkit for neural machine translation"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "minimum word error rate training for attention based sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "assessing crosslingual discourse relations in machine translation"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "distributed representations of words and phrases and their compositionality"

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)