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: "why not be versatile applications of the sgnmt decoder for machine translation"
  Abstract: SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In this paper, we describe three use cases in which SGNMT is currently playing an active role: (1) teaching as SGNMT is being used for course work and student theses in the MPhil in Machine Learning, Speech and Language Technology at the University of Cambridge, (2) research as most of the research work of the Cambridge MT group is based on SGNMT, and (3) technology transfer as we show how SGNMT is helping to transfer research findings from the laboratory to the industry, eg. into a product of SDL plc.

Connected through hyperedge HE-1 (induced by source paper "cued wmt19 ewc lms"):
  with members:
    - "selective attention for context aware neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "improving the transformer translation model with document level context"
    - "simple fusion return of the language model"

Connected through hyperedge HE-2 (induced by source paper "on nmt search errors and model errors cat got your tongue"):
  with members:
    - "calibration of encoder decoder models for neural machine translation"
    - "the university of cambridge s machine translation systems for wmt18"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "correcting length bias in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "analyzing neural mt search and model performance"
    - "attention is all you need"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"

Connected through hyperedge HE-3 (induced by source paper "the cued s grammatical error correction systems for bea 2019"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "neural grammatical error correction with finite state transducers"
    - "investigating backtranslation in neural machine translation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "the university of cambridge s machine translation systems for wmt18"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "understanding back translation at scale"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "the university of cambridge s machine translation systems for wmt18"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "weighted transformer network for machine translation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "self attention with relative position representations"
    - "training tips for the transformer model"
    - "fine grained evaluation of german english machine translation based on a test suite"
    - "accelerating nmt batched beam decoding with lmbr posteriors for deployment"
    - "the best of both worlds combining recent advances in 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)