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: "implicitly incorporating morphological information into word embedding"
  Abstract: In this paper, we implicitly incorporate morpheme information into word embedding. Based on the strategy we utilize the morpheme information, three models are proposed. To test the performances of our models, we conduct the word similarity and syntactic analogy. The results demonstrate the effectiveness of our methods. Our models beat the comparative baselines on both tasks to a great extent. On the golden standard Wordsim-353 and RG-65, our models approximately outperform CBOW for 5 and 7 percent, respectively. In addition, 7 percent advantage is also achieved by our models on syntactic analysis. According to parameter analysis, our models can increase the semantic information in the corpus and our performances on the smallest corpus are similar to the performance of CBOW on the corpus which is five times ours. This property of our methods may have some positive effects on NLP researches about the corpus-limited languages.

Connected through hyperedge HE-1 (induced by source paper "adapting word embeddings to new languages with morphological and phonological subword representations"):
  with members:
    - "morphological word embeddings"
    - "a survey of cross lingual word embedding models"
    - "xnmt the extensible neural machine translation toolkit"
    - "deep contextualized word representations"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "learning crosslingual word embeddings without bilingual corpora"

Connected through hyperedge HE-2 (induced by source paper "second order word embeddings from nearest neighbor topological features"):
  with members:
    - "the interplay of semantics and morphology in word embeddings"
    - "cross lingual models of word embeddings an empirical comparison"
    - "long short term memory networks for machine reading"
    - "the role of context types and dimensionality in learning word embeddings"
    - "node2vec scalable feature learning for networks"
    - "ultradense word embeddings by orthogonal transformation"
    - "understanding neural networks through representation erasure"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "morphological embeddings for named entity recognition in morphologically rich languages"):
  with members:
    - "morphological priors for probabilistic neural word embeddings"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)