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: "neural morphological tagging from characters for morphologically rich languages"
  Abstract: This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting. We explore supplementary use of word-based vectors trained on large amounts of unlabeled data. Our experiments for morphological tagging suggest that for "simple" model configurations, the choice of the network architecture (CNN vs. CNNHighway vs. LSTM vs. BLSTM) or the augmentation with pre-trained word embeddings can be important and clearly impact the accuracy. Increasing the model capacity by adding depth, for example, and carefully optimizing the neural networks can lead to substantial improvements, and the differences in accuracy (but not training time) become much smaller or even negligible. Overall, our best morphological taggers for German and Czech outperform the best results reported in the literature by a large margin.

Connected through hyperedge HE-1 (induced by source paper "neural factor graph models for cross lingual morphological tagging"):
  with members:
    - "morphological segmentation inside out"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "cross lingual morphological tagging for low resource languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-2 (induced by source paper "cross lingual character level neural morphological tagging"):
  with members:
    - "one shot neural cross lingual transfer for paradigm completion"
    - "on the state of the art of evaluation in neural language models"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "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"
    - "character level convolutional networks for text classification"

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)