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: "predicting target language ccg supertags improves neural machine translation"
  Abstract: Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask training? We introduce syntactic information in the form of CCG supertags in the decoder, by interleaving the target supertags with the word sequence. Our results on WMT data show that explicitly modeling target-syntax improves machine translation quality for German->English, a high-resource pair, and for Romanian->English, a low-resource pair and also several syntactic phenomena including prepositional phrase attachment. Furthermore, a tight coupling of words and syntax improves translation quality more than multitask training. By combining target-syntax with adding source-side dependency labels in the embedding layer, we obtain a total improvement of 0.9 BLEU for German->English and 1.2 BLEU for Romanian->English.

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 "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "assessing bert s syntactic abilities"
    - "universal transformers"
    - "finding syntax in human encephalography with beam search"
    - "distilling knowledge for search based structured prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "promoting the knowledge of source syntax in transformer nmt is not needed"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "training tips for the transformer model"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "scheduled multi task learning from syntax to translation"
    - "linguistically informed self attention for semantic role labeling"
    - "learning to parse and translate improves neural machine translation"
    - "attention is all you need"
    - "modeling target side inflection in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural generation for czech data and baselines"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "end to end content and plan selection for data to text generation"
    - "rdf2pt generating brazilian portuguese texts from rdf data"
    - "are all languages equally hard to language model"
    - "modeling target side inflection in neural machine translation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "topic aware neural response generation"

Connected through hyperedge HE-5 (induced by source paper "multi source syntactic neural machine translation"):
  with members:
    - "neural machine translation with source side latent graph parsing"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "tree to sequence attentional neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-6 (induced by source paper "exploiting semantics in neural machine translation with graph convolutional networks"):
  with members:
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "neural message passing for quantum chemistry"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "a convolutional encoder model for neural machine translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "discrete structural planning for neural machine translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "towards string to tree neural machine translation"
    - "compressing word embeddings via deep compositional code learning"
    - "a simple fast diverse decoding algorithm for neural generation"
    - "key value memory networks for directly reading documents"
    - "syntactically guided neural machine translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-8 (induced by source paper "multi representation ensembles and delayed sgd updates improve syntax based nmt"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "attention is all you need"
    - "don t decay the learning rate increase the batch size"
    - "towards string to tree neural machine translation"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "recurrent neural network grammars"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

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)