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: "learning chinese word representations from glyphs of characters"
  Abstract: In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public.

Connected through hyperedge HE-1 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (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-3 (induced by source paper "text classification with pixel embedding"):
  with members:
    - "vcwe visual character enhanced word embeddings"
    - "understanding convolutional neural networks for text classification"
    - "learning character level compositionality with visual features"
    - "radical level ideograph encoder for rnn based sentiment analysis of chinese and japanese"
    - "a structured self attentive sentence embedding"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "simple baseline for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"):
  with members:
    - "mathematics content understanding for cyberlearning via formula evolution map"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross language citation recommendation via hierarchical representation learning on heterogeneous graph"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-5 (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"
    - "glyce glyph vectors for chinese character representations"
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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-6 (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 character level compositionality with visual features"
    - "interactive attention networks for aspect level sentiment classification"
    - "component enhanced chinese character embeddings"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "vcwe visual character enhanced word embeddings"):
  with members:
    - "learning character level compositionality with visual features"
    - "a structured self attentive sentence embedding"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "chinese embedding via stroke and glyph information a dual channel view"):
  with members:
    - "morphological word embeddings"
    - "glyce glyph vectors for chinese character representations"
    - "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)