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: "neural reranking improves subjective quality of machine translation naist at wat2015"
  Abstract: This year, the Nara Institute of Science and Technology (NAIST)'s submission to the 2015 Workshop on Asian Translation was based on syntax-based statistical machine translation, with the addition of a reranking component using neural attentional machine translation models. Experiments re-confirmed results from previous work stating that neural MT reranking provides a large gain in objective evaluation measures such as BLEU, and also confirmed for the first time that these results also carry over to manual evaluation. We further perform a detailed analysis of reasons for this increase, finding that the main contributions of the neural models lie in improvement of the grammatical correctness of the output, as opposed to improvements in lexical choice of content words.

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 "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-3 (induced by source paper "why not be versatile applications of the sgnmt decoder for machine translation"):
  with members:
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "unfolding and shrinking neural machine translation ensembles"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"
    - "syntactically guided neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation with source side latent graph parsing"):
  with members:
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "learning to parse and translate improves neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "tree to sequence attentional neural machine translation"
    - "enriching word vectors with subword information"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "dependency parsing as head selection"

Connected through hyperedge HE-5 (induced by source paper "unfolding and shrinking neural machine translation ensembles"):
  with members:
    - "qcri machine translation systems for iwslt 16"
    - "ensemble distillation for neural machine translation"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "noiseout a simple way to prune neural networks"
    - "learning compact recurrent neural networks"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-6 (induced by source paper "improving neural machine translation through phrase based forced decoding"):
  with members:
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "six challenges for neural machine translation"
    - "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"
    - "coverage embedding models for neural machine translation"
    - "interactive attention for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with external phrase memory"
    - "syntactically guided neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "handling homographs in neural machine translation"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "context dependent word representation for neural machine translation"
    - "semi supervised word sense disambiguation with neural models"
    - "word sense disambiguation using a bidirectional lstm"
    - "effective approaches to attention based neural machine translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"):
  with members:
    - "character based neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "the edit distance transducer in action the university of cambridge english german system at wmt16"
    - "bridging neural machine translation and bilingual dictionaries"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "mutual information and diverse decoding improve neural machine translation"
    - "syntactically guided 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)