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: "supercaptioning image captioning using two dimensional word embedding"
  Abstract: Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop.

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"
    - "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"
    - "super characters a conversion from sentiment classification to image classification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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"

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)