human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "lexicon integrated cnn models with attention for sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification")
  Member "character aware neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "retrofitting word vectors to semantic lexicons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")

Question: Please tell me which class the source paper 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)