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: "which encoding is the best for text classification in chinese english japanese and korean"
  Abstract: This article offers an empirical study on the different ways of encoding Chinese, Japanese, Korean (CJK) and English languages for text classification. Different encoding levels are studied, including UTF-8 bytes, characters, words, romanized characters and romanized words. For all encoding levels, whenever applicable, we provide comparisons with linear models, fastText and convolutional networks. For convolutional networks, we compare between encoding mechanisms using character glyph images, one-hot (or one-of-n) encoding, and embedding. In total there are 473 models, using 14 large-scale text classification datasets in 4 languages including Chinese, English, Japanese and Korean. Some conclusions from these results include that byte-level one-hot encoding based on UTF-8 consistently produces competitive results for convolutional networks, that word-level n-grams linear models are competitive even without perfect word segmentation, and that fastText provides the best result using character-level n-gram encoding but can overfit when the features are overly rich.

Connected through hyperedge HE-1 (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-2 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "is word segmentation necessary for deep learning of chinese representations"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bag of words as target for neural machine translation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-4 (induced by source paper "a multilingual encoding method for text classification and dialect identification using convolutional neural network"):
  with members:
    - "very deep convolutional networks for text classification"
    - "bag of tricks for efficient text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "character level convolutional networks for text classification"
    - "a critical review of recurrent neural networks for sequence learning"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "deep learning in neural networks"
    - "improved transition based parsing by modeling characters instead of words with lstms"

Connected through hyperedge HE-5 (induced by source paper "uer an open source toolkit for pre training models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"
    - "ncrf an open source neural sequence labeling toolkit"

Connected through hyperedge HE-6 (induced by source paper "investigating an effective character level embedding in korean sentence classification"):
  with members:
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "real time automatic word segmentation for user generated text"
    - "attention is all you need"
    - "a sub character architecture for korean language processing"
    - "a structured self attentive sentence embedding"
    - "tweet2vec character based distributed representations for social media"
    - "neural language correction with character based attention"

Connected through hyperedge HE-7 (induced by source paper "an investigation of transfer learning based sentiment analysis in japanese"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "super characters a conversion from sentiment classification to image classification"
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-8 (induced by source paper "system demo for transfer learning across vision and text using domain specific cnn accelerator for on device nlp applications"):
  with members:
    - "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"
    - "supertml two dimensional word embedding for the precognition on structured tabular data"
    - "supercaptioning image captioning using two dimensional word embedding"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
    - "super characters a conversion from sentiment classification to image classification"
    - "character level convolutional networks for text classification"
    - "very deep convolutional networks for large scale image recognition"

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)