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: "morphological inflection generation using character sequence to sequence learning"
  Abstract: Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "demographic inference and representative population estimates from multilingual social media data"):
  with members:
    - "on the convergence of adam and beyond"
    - "bleaching text abstract features for cross lingual gender prediction"
    - "co training for demographic classification using deep learning from label proportions"
    - "word translation without parallel data"
    - "densely connected convolutional networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "whom should we sense in social sensing analyzing which users work best for social media now casting"
    - "character aware neural language models"

Connected through hyperedge HE-3 (induced by source paper "unsupervised inflection generation using neural language modeling"):
  with members:
    - "universal language model fine tuning for text classification"

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"
    - "modeling target side inflection in neural machine translation"
    - "neural multi source morphological reinflection"
    - "neural language correction with character based attention"

Connected through hyperedge HE-5 (induced by source paper "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "a structured variational autoencoder for contextual morphological inflection"):
  with members:
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "enriching word vectors with subword information"
    - "effective approaches to attention based neural machine translation"
    - "reweighted wake sleep"
    - "conditional random field autoencoders for unsupervised structured prediction"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "unsupervised disambiguation of syncretism in inflected lexicons"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "a universal part of speech tagset"

Connected through hyperedge HE-8 (induced by source paper "one shot neural cross lingual transfer for paradigm completion"):
  with members:
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "single model encoder decoder with explicit morphological representation for reinflection"
    - "cross lingual morphological tagging for low resource languages"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "multi task sequence to sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"

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)