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: "cross pollination of information in online social media a case study on popular social networks"
  Abstract: Owing to the popularity of Online Social Media (OSM), Internet users share a lot of information (including personal) on and across OSM services every day. For example, it is common to find a YouTube video embedded in a blog post with an option to share the link on Facebook. Users recommend, comment, and forward information they receive from friends, contributing in spreading the information in and across OSM services. We term this information diffusion process from one OSM service to another as Cross-Pollination, and the network formed by users who participate in Cross-Pollination and content produced in the network as \emph{Cross-Pollinated network}. Research has been done about information diffusion within one OSM service, but little is known about Cross-Pollination. In this paper, we aim at filling this gap by studying how information (video, photo, location) from three popular OSM services (YouTube, Flickr and Foursquare) diffuses on Twitter, the most popular microblogging service. Our results show that Cross-Pollinated networks follow temporal and topological characteristics of the diffusion OSM (Twitter in our study). Furthermore, popularity of information on source OSM (YouTube, Flickr and Foursquare) does not imply its popularity on Twitter. Our results also show that Cross-Pollination helps Twitter in terms of traffic generation and user involvement, but only a small fraction of videos and photos gain a significant number of views from Twitter. We believe this is the first research work which explicitly characterizes the diffusion of information across different OSM services.

Connected through hyperedge HE-1 (induced by source paper "indian premier league ipl cricket online social media"):
  with members:
    - "trends in social media persistence and decay"
    - "harvesting collective intelligence temporal behavior in yahoo answers"
    - "predicting the popularity of online content"

Connected through hyperedge HE-2 (induced by source paper "finding nemo searching and resolving identities of users across online social networks"):
  with members:
    - "studying user footprints in different online social networks"
    - "how unique and traceable are usernames"
    - "de anonymizing social networks"

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)