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 character level compositionality with visual features"
  Abstract: Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.

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 processing like humans do visually attacking and shielding nlp systems"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "shielding google s language toxicity model against adversarial attacks"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "deceiving google s perspective api built for detecting toxic comments"

Connected through hyperedge HE-4 (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 chinese word representations from glyphs of characters"
    - "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-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 chinese word representations from glyphs of characters"
    - "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 chinese word representations from glyphs of characters"
    - "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 chinese word representations from glyphs of characters"
    - "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 "apply chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "improving character level japanese chinese neural machine translation with radicals as an additional input feature"
    - "imagination improves multimodal translation"
    - "word subword or character an empirical study of granularity in chinese english nmt"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "multilingual multi modal embeddings for natural language processing"
    - "towards string to tree neural machine translation"
    - "six challenges for neural machine translation"
    - "context gates for neural machine translation"

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)