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: "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
  Abstract: This paper introduces SGNMT, our experimental platform for machine translation research. SGNMT provides a generic interface to neural and symbolic scoring modules (predictors) with left-to-right semantic such as translation models like NMT, language models, translation lattices, $n$-best lists or other kinds of scores and constraints. Predictors can be combined with other predictors to form complex decoding tasks. SGNMT implements a number of search strategies for traversing the space spanned by the predictors which are appropriate for different predictor constellations. Adding new predictors or decoding strategies is particularly easy, making it a very efficient tool for prototyping new research ideas. SGNMT is actively being used by students in the MPhil program in Machine Learning, Speech and Language Technology at the University of Cambridge for course work and theses, as well as for most of the research work in our group.

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"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "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"

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"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "understanding back translation at scale"

Connected through hyperedge HE-4 (induced by source paper "neural grammatical error correction with finite state transducers"):
  with members:
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "adapting sequence models for sentence correction"
    - "grammatical error correction with neural reinforcement learning"
    - "neural network translation models for grammatical error correction"

Connected through hyperedge HE-5 (induced by source paper "domain adaptive inference for neural machine translation"):
  with members:
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "regularization techniques for fine tuning in neural machine translation"
    - "six challenges for neural machine translation"
    - "neural machine translation training in a multi domain scenario"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-6 (induced by source paper "ucam biomedical translation at wmt19 transfer learning multi domain ensembles"):
  with members:
    - "domain adaptive inference for neural machine translation"
    - "cued wmt19 ewc lms"
    - "a call for clarity in reporting bleu scores"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"

Connected through hyperedge HE-7 (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"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "fine grained evaluation of german english machine translation based on a test suite"
    - "accelerating nmt batched beam decoding with lmbr posteriors for deployment"

Connected through hyperedge HE-8 (induced by source paper "an operation sequence model for explainable neural machine translation"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "training tips for the transformer model"
    - "neural machine translation decoding with terminology constraints"
    - "qcri machine translation systems for iwslt 16"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "six challenges for 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)