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: "evolution of fashion brands on twitter and instagram"
  Abstract: Social media has become a popular platform for marketing and brand advertisement especially for fashion brands. To promote their products and gain popularity, different brands post their latest products and updates as photos on different social networks. Little has been explored on how these fashion brands use different social media to reach out to their customers and obtain feedback on different products. Understanding this can help future consumers to focus on their interested brands on specific social media for better information and also can help the marketing staff of different brands to understand how the other brands are utilizing the social media. In this article we focus on the top-20 fashion brands and comparatively analyze how they target their current and potential future customers on Twitter and Instagram. Using both linguistic and deep image features, our work reveals an increasing diversification of trends accompanied by a simultaneous concentration towards a few selected trends. It provides insights about brand marketing strategies and their respective competencies. Our investigations show that the brands are using Twitter and Instagram in a distinctive manner.

Connected through hyperedge HE-1 (induced by source paper "woman metal white vs man dress shorts combining social temporal and image signals to understand popularity of pinterest fashion boards"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "cultural diffusion and trends in facebook photographs"
    - "pinterest board recommendation for twitter users"
    - "densecap fully convolutional localization networks for dense captioning"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"

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.SI(Social and Information Networks)