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: "understanding spreading patterns on social networks based on network topology"
  Abstract: Ever since the proposal of first epidemic model, scientists have been attempting to estimate the growth of a disease/contagion while in its premature stage. Despite being the focus of researchers for a long time, understanding epidemiology remains as error prone as a weather forecast, mainly because of the unavailability of large amount of data. An epidemic spread is analogous to the diffusion of memes in social networking sites. Diffusion of memes can be easily studied provided large datasets and computational powers to extract information from online networks. So, studying a meme spreading pattern can help us in understanding epidemiology. In this paper, we analyse the impact of the topology of a social network, specifically its meso scale properties- community structure and core-periphery structure, on a meme traversing over it. We propose a meme propagation model for synthetic scale free graphs which resemble real world graphs and observe the process of a meme going viral on such a network. We also validate our model for real world information propagation.

Connected through hyperedge HE-1 (induced by source paper "estimating shell index in a graph with local information"):
  with members:
    - "global rank estimation"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "modeling memetics using edge diversity"
    - "pseudo cores the terminus of an intelligent viral meme s trajectory"
    - "rank me thou shalln t compare me"
    - "evolving models for meso scale structures"
    - "estimating the degree centrality ranking of a node"

Connected through hyperedge HE-2 (induced by source paper "global rank estimation"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "literature survey on interplay of topics information diffusion and connections on social networks"
    - "group centrality for semantic networks a swot analysis featuring random walks"
    - "centrality measures in networks based on nodes attributes long range interactions and group influence"
    - "rank me thou shalln t compare me"
    - "fully dynamic approximation of betweenness centrality"

Connected through hyperedge HE-3 (induced by source paper "degree ranking using local information"):
  with members:
    - "a faster method to estimate closeness centrality ranking"
    - "ant inspired density estimation via random walks"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "rank me thou shalln t compare me"
    - "estimating the degree centrality ranking of a node"
    - "walk not wait faster sampling over online social networks"
    - "real time enhanced random sampling of online social networks"
    - "graph size estimation"

Connected through hyperedge HE-4 (induced by source paper "modeling memetics using edge diversity"):
  with members:
    - "predicting successful memes using network and community structure"
    - "virality prediction and community structure in social networks"
    - "exploring text virality in social networks"
    - "the role of social networks in information diffusion"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-5 (induced by source paper "pseudo cores the terminus of an intelligent viral meme s trajectory"):
  with members:
    - "virality prediction and community structure in social networks"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "an o m algorithm for cores decomposition of networks"
    - "search in power law networks"

Connected through hyperedge HE-6 (induced by source paper "rank me thou shalln t compare me"):
  with members:
    - "modeling memetics using edge diversity"
    - "evolving models for meso scale structures"
    - "estimating the degree centrality ranking of a node"
    - "graph size estimation"

Connected through hyperedge HE-7 (induced by source paper "shifting behaviour of users towards understanding the fundamental law of social networks"):
  with members:
    - "the majority illusion in social networks"
    - "epidemiological modeling of online social network dynamics"

Connected through hyperedge HE-8 (induced by source paper "evolving models for meso scale structures"):
  with members:
    - "pseudo cores the terminus of an intelligent viral meme s trajectory"

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)