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: "glyce glyph vectors for chinese character representations"
  Abstract: It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages. However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found. In this paper, we address this gap by presenting Glyce, the glyph-vectors for Chinese character representations. We make three major innovations: (1) We use historical Chinese scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to enrich the pictographic evidence in characters; (2) We design CNN structures (called tianzege-CNN) tailored to Chinese character image processing; and (3) We use image-classification as an auxiliary task in a multi-task learning setup to increase the model's ability to generalize. We show that glyph-based models are able to consistently outperform word/char ID-based models in a wide range of Chinese NLP tasks. We are able to set new state-of-the-art results for a variety of Chinese NLP tasks, including tagging (NER, CWS, POS), sentence pair classification, single sentence classification tasks, dependency parsing, and semantic role labeling. For example, the proposed model achieves an F1 score of 80.6 on the OntoNotes dataset of NER, +1.5 over BERT; it achieves an almost perfect accuracy of 99.8\% on the Fudan corpus for text classification. Code found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "fgn fusion glyph network for chinese named entity recognition"):
  with members:
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "attention is all you need"
    - "glyph aware embedding of chinese characters"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "dice loss for data imbalanced nlp tasks"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "libra r cnn towards balanced learning for object detection"
    - "towards accurate one stage object detection with ap loss"
    - "dsreg using distant supervision as a regularizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "chinese ner using lattice lstm"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-3 (induced by source paper "probing contextualized sentence representations with visual awareness"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "semantics aware bert for language understanding"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "sg net syntax guided machine reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-4 (induced by source paper "a unified mrc framework for named entity recognition"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "nested named entity recognition via second best sequence learning and decoding"
    - "sequence to nuggets nested entity mention detection via anchor region networks"
    - "merge and label a novel neural network architecture for nested ner"
    - "neural architectures for nested ner through linearization"
    - "a general framework for information extraction using dynamic span graphs"
    - "chinese ner using lattice lstm"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-5 (induced by source paper "query based named entity recognition"):
  with members:
    - "chinese ner using lattice lstm"
    - "the natural language decathlon multitask learning as question answering"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "deep contextualized word representations"
    - "a neural transition based model for nested mention recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot relation extraction via reading comprehension"
    - "dcn mixed objective and deep residual coattention for question answering"

Connected through hyperedge HE-6 (induced by source paper "using chinese glyphs for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "a multi task learning approach for named entity recognition using local detection"
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning chinese word representations from glyphs of characters"
    - "learning character level compositionality with visual features"
    - "glyph aware embedding of chinese characters"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-7 (induced by source paper "chinese embedding via stroke and glyph information a dual channel view"):
  with members:
    - "morphological word embeddings"
    - "learning chinese word representations from glyphs of characters"
    - "a survey on network embedding"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "efficient estimation of word representations in vector space"

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)