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: "topic diffusion and emergence of virality in social networks"
  Abstract: We propose a stochastic model for the diffusion of topics entering a social network modeled by a Watts-Strogatz graph. Our model sets into play an implicit competition between these topics as they vie for the attention of users in the network. The dynamics of our model are based on notions taken from real-world OSNs like Twitter where users either adopt an exogenous topic or copy topics from their neighbors leading to endogenous propagation. When instantiated correctly, the model achieves a viral regime where a few topics garner unusually good response from the network, closely mimicking the behavior of real-world OSNs. Our main contribution is our description of how clusters of proximate users that have spoken on the topic merge to form a large giant component making a topic go viral. This demonstrates that it is not weak ties but actually strong ties that play a major part in virality. We further validate our model and our hypotheses about its behavior by comparing our simulation results with the results of a measurement study conducted on real data taken from Twitter.

Connected through hyperedge HE-1 (induced by source paper "viral content propagation in online social networks"):
  with members:
    - "exploring text virality in social networks"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-2 (induced by source paper "on the role of conductance geography and topology in predicting hashtag virality"):
  with members:
    - "can cascades be predicted"
    - "a bayesian approach for predicting the popularity of tweets"
    - "predicting successful memes using network and community structure"
    - "exploring image virality in google plus"
    - "virality prediction and community structure in social networks"
    - "exploring text virality in social networks"
    - "information diffusion and external influence in networks"
    - "good friends bad news affect and virality in twitter"

Connected through hyperedge HE-3 (induced by source paper "lifespan and propagation of information in on line social networks a case study"):
  with members:
    - "predicting the temporal dynamics of information diffusion in social networks"
    - "exploring text virality in social networks"
    - "the role of social networks in information diffusion"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "good friends bad news affect and virality in twitter"
    - "influence and passivity in social media"

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)