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 the influence of all nodes in a network"
  Abstract: Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the expected force, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large.

Connected through hyperedge HE-1 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

Connected through hyperedge HE-2 (induced by source paper "link transmission centrality in large scale social networks"):
  with members:
    - "detecting global bridges in networks"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "on facebook most ties are weak"
    - "a novel measure of edge centrality in social networks"

Connected through hyperedge HE-3 (induced by source paper "understanding and forecasting lifecycle events in information cascades"):
  with members:
    - "toward order of magnitude cascade prediction"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "limited attention and centrality in social networks"
    - "rethinking centrality the role of dynamical processes in social network analysis"

Connected through hyperedge HE-4 (induced by source paper "a comparison of spatial based targeted disease containment strategies using mobile phone data"):
  with members:
    - "an analytical framework to nowcast well being using mobile phone data"

Connected through hyperedge HE-5 (induced by source paper "benchmarking measures of network influence"):
  with members:
    - "an introduction to temporal graphs an algorithmic perspective"

Connected through hyperedge HE-6 (induced by source paper "measuring the potential of individual airports for pandemic spread over the world airline network"):
  with members:
    - "localization and centrality in 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)