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: "modeling target side inflection in neural machine translation"
  Abstract: NMT systems have problems with large vocabulary sizes. Byte-pair encoding (BPE) is a popular approach to solving this problem, but while BPE allows the system to generate any target-side word, it does not enable effective generalization over the rich vocabulary in morphologically rich languages with strong inflectional phenomena. We introduce a simple approach to overcome this problem by training a system to produce the lemma of a word and its morphologically rich POS tag, which is then followed by a deterministic generation step. We apply this strategy for English-Czech and English-German translation scenarios, obtaining improvements in both settings. We furthermore show that the improvement is not due to only adding explicit morphological information.

Connected through hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-2 (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"
    - "predicting target language ccg supertags improves neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "predicting target language ccg supertags improves 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-4 (induced by source paper "neural transductive learning and beyond morphological generation in the minimal resource setting"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "unlabeled data for morphological generation with character based sequence to sequence models"
    - "neural multi source morphological reinflection"
    - "neural language correction with character based attention"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-5 (induced by source paper "optimizing segmentation granularity for neural machine translation"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "lifelong learning with dynamically expandable networks"
    - "linguistically motivated vocabulary reduction for neural machine translation from turkish to english"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"

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)