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: "unlabeled data for morphological generation with character based sequence to sequence models"
  Abstract: We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection, the task of generating one inflected word form from another. This is achieved by using unlabeled tokens or random strings as training data for an autoencoding task, adapting a network for morphological reinflection, and performing multi-task training. We thus use limited labeled data more effectively, obtaining up to 9.9% improvement over state-of-the-art baselines for 8 different languages.

Connected through hyperedge HE-1 (induced by source paper "universal lemmatizer a sequence to sequence model for lemmatizing universal dependencies treebanks"):
  with members:
    - "universal dependency parsing from scratch"
    - "marrying universal dependencies and universal morphology"
    - "su rug at the conll sigmorphon 2017 shared task morphological inflection with attentional sequence to sequence models"
    - "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"
    - "opennmt open source toolkit for neural machine translation"
    - "deep biaffine attention for neural dependency parsing"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "data augmentation for context sensitive neural lemmatization using inflection tables and raw text"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "marrying universal dependencies and universal morphology"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-3 (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"
    - "modeling target side inflection in neural machine translation"
    - "neural multi source morphological reinflection"
    - "neural language correction with character based attention"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

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)