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 "fgn fusion glyph network for chinese named entity recognition"):
  Abstract: Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information, which is often overlooked. We propose the FGN, Fusion Glyph Network for Chinese NER. This method may offer glyph information for fusion representation learning with BERT. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs. (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.

Includes members:
  Member "glyce glyph vectors for chinese character representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dice loss for data imbalanced nlp tasks")
  Member "chinese ner using lattice lstm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self attention based bigru and capsule network for named entity recognition")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    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 "memory fusion network for multi view sequential learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "emotions don t lie a deepfake detection method using audio visual affective cues")
  Member "tensor fusion network for multimodal sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis")
  Member "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "glyph aware embedding of chinese characters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")

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)