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: "component enhanced chinese character embeddings"
  Abstract: Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.

Connected through hyperedge HE-1 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "a survey on recent advances in named entity recognition from deep learning models"):
  with members:
    - "neuroner an easy to use program for named entity recognition based on neural networks"
    - "end to end recurrent neural network models for vietnamese named entity recognition word level vs character level"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "an investigation of recurrent neural architectures for drug name recognition"
    - "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"
    - "multi task cross lingual sequence tagging from scratch"

Connected through hyperedge HE-3 (induced by source paper "phonetic enriched text representation for chinese sentiment analysis with reinforcement learning"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "tensor fusion network for multimodal sentiment analysis"
    - "learning chinese word representations from glyphs of characters"
    - "learning character level compositionality with visual features"
    - "interactive attention networks for aspect level sentiment classification"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "dual long short term memory networks for sub character representation learning"):
  with members:
    - "adversarial multi criteria learning for chinese word segmentation"
    - "mimicking word embeddings using subword rnns"
    - "fast and accurate neural word segmentation for chinese"
    - "enriching word vectors with subword information"
    - "neural word segmentation learning for chinese"
    - "neural architectures for named entity recognition"
    - "character aware neural language models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "enhancing chinese intent classification by dynamically integrating character features into word embeddings with ensemble techniques"):
  with members:
    - "enriching word vectors with subword information"
    - "character aware neural language models"
    - "boosting named entity recognition with neural character embeddings"
    - "radical enhanced chinese character embedding"

Connected through hyperedge HE-6 (induced by source paper "improving word vector with prior knowledge in semantic dictionary"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "learning character level compositionality with visual features"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "what you get is what you see a visual markup decompiler"
    - "visual7w grounded question answering in images"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "skip thought vectors"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "learning chinese word representations from glyphs of characters"):
  with members:
    - "learning character level compositionality with visual features"
    - "skip thought vectors"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "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)