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 "multiple character embeddings for chinese word segmentation"):
  Abstract: Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great performance by leveraging powerful neural networks. However, these works neglect an important clue: Chinese characters contain both semantic and phonetic meanings. In this paper, we introduce multiple character embeddings including Pinyin Romanization and Wubi Input, both of which are easily accessible and effective in depicting semantics of characters. To fully leverage them, we propose a novel shared Bi-LSTM-CRF model, which fuses multiple features efficiently. Extensive experiments on five corpora demonstrate that extra embeddings help obtain a significant improvement. Specifically, we achieve the state-of-the-art performance in AS and CityU datasets with F1 scores 96.9 and 97.3, respectively without leveraging any external resources.

Includes members:
  Member "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fgn fusion glyph network for chinese named entity recognition")
  Member "adversarial multi criteria learning for chinese word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text")
  Member "a gap based framework for chinese word segmentation via very deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "char rnn and active learning for hashtag segmentation")
  Member "convolutional neural network with word embeddings for chinese word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "can ner convolutional attention network for chinese named entity recognition")
  Member "fast and accurate neural word segmentation for chinese":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "neural word segmentation with rich pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "bi directional lstm recurrent neural network for chinese word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "is word segmentation necessary for deep learning of chinese representations")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")

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)