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: "how grammatical is character level neural machine translation assessing mt quality with contrastive translation pairs"
  Abstract: Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval97, a large-scale data set of 97000 contrastive translation pairs based on the WMT English->German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning.

Connected through hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-2 (induced by source paper "a survey on document level machine translation methods and evaluation"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "findings of the third workshop on neural generation and translation"
    - "naver labs europe s systems for the document level generation and translation task at wngt 2019"
    - "improving conditioning in context aware sequence to sequence models"
    - "evaluating pronominal anaphora in machine translation an evaluation measure and a test suite"
    - "using whole document context in neural machine translation"
    - "english czech systems in wmt19 document level transformer"
    - "when and why is document level context useful in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "subword language model for query auto completion"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "personalized neural language models for real world query auto completion"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning to discover ground and use words with segmental neural language models"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "personalized language model for query auto completion"
    - "distilling knowledge for search based structured prediction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-4 (induced by source paper "no training required exploring random encoders for sentence classification"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"

Connected through hyperedge HE-5 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-6 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-7 (induced by source paper "context in neural machine translation a review of models and evaluations"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "a latent morphology model for open vocabulary neural machine translation"):
  with members:
    - "on the importance of word boundaries in character level neural machine translation"
    - "interpretable neural predictions with differentiable binary variables"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "are all languages equally hard to language model"
    - "character level models versus morphology in semantic role labeling"
    - "from characters to words to in between do we capture morphology"
    - "opennmt open source toolkit 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)