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: "attention competition with advertisement"
  Abstract: In the new digital age, information is available in large quantities. Since information consumes primarily the attention of its recipients, the scarcity of attention is becoming the main limiting factor. In this study, we investigate the impact of advertisement pressure on a cultural market where consumers have a limited attention capacity. A model of competition for attention is developed and investigated analytically and by simulation. Advertisement is found to be much more effective when attention capacity of agents is extremely scarce. We have observed that the market share of the advertised item improves if dummy items are introduced to the market while the strength of the advertisement is kept constant.

Connected through hyperedge HE-1 (induced by source paper "temporal pattern of online communication spike trains in spreading a scientific rumor how often who interacts with whom"):
  with members:
    - "the majority illusion in social networks"
    - "local variation of hashtag spike trains and popularity in twitter"
    - "local variation of collective attention in hashtag spike trains"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "online popularity and topical interests through the lens of instagram"
    - "modelling of trends in twitter using retweet graph dynamics"
    - "the bursty dynamics of the twitter information network"
    - "how random are online social interactions"

Connected through hyperedge HE-2 (induced by source paper "local variation of hashtag spike trains and popularity in twitter"):
  with members:
    - "the bursty dynamics of the twitter information network"
    - "word storms multiples of word clouds for visual comparison of documents"
    - "virality prediction and community structure in social networks"
    - "word statistics in blogs and rss feeds towards empirical universal evidence"

Connected through hyperedge HE-3 (induced by source paper "local variation of collective attention in hashtag spike trains"):
  with members:
    - "local variation of hashtag spike trains and popularity in twitter"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"

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)