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: "super characters a conversion from sentiment classification to image classification"
  Abstract: We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English.

Connected through hyperedge HE-1 (induced by source paper "multi modal sentiment analysis using super characters method on low power cnn accelerator device"):
  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"
    - "system demo for transfer learning across vision and text using domain specific cnn accelerator for on device nlp applications"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "happydb a corpus of 100 000 crowdsourced happy moments"

Connected through hyperedge HE-2 (induced by source paper "supertml two dimensional word embedding for the precognition on structured tabular data"):
  with members:
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "neural feature learning from relational database"
    - "deep contextualized word representations"
    - "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "catboost unbiased boosting with categorical features"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "supercaptioning image captioning using two dimensional word embedding"):
  with members:
    - "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"
    - "supertml two dimensional word embedding and transfer learning using imagenet pretrained cnn models for the classifications on tabular data"
    - "supertml two dimensional word embedding for the precognition on structured tabular data"
    - "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"
    - "a comprehensive survey of deep learning for image captioning"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"):
  with members:
    - "supertml two dimensional word embedding and transfer learning using imagenet pretrained cnn models for the classifications on tabular data"
    - "squared english word a method of generating glyph to use super characters for sentiment analysis"
    - "generating multiple diverse responses for short text conversation"
    - "ultra power efficient cnn domain specific accelerator with 9 3tops watt for mobile and embedded applications"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "squeeze and excitation networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (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"
    - "learned in translation contextualized word vectors"
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "a structured self attentive sentence embedding"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-6 (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"
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "character level convolutional networks for text classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "squared english word a method of generating glyph to use super characters for sentiment analysis"):
  with members:
    - "happydb a corpus of 100 000 crowdsourced happy moments"
    - "attention is all you need"
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "xgboost a scalable tree boosting system"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "neural machine translation by jointly learning to align and translate"

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)