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: "superchat dialogue generation by transfer learning from vision to language using two dimensional word embedding"
  Abstract: The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational response for open domain dialogues. The experimental results on a public dataset shows that the proposed SuperChat method generates high quality responses. 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:
    - "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"
    - "super characters a conversion from sentiment classification to image classification"

Connected through hyperedge HE-2 (induced by source paper "supercaptioning image captioning 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"
    - "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"
    - "super characters a conversion from sentiment classification to image classification"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (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:
    - "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"
    - "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)