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: "campaigning in heterogeneous social networks optimal control of si information epidemics"
  Abstract: We study the optimal control problem of maximizing the spread of an information epidemic on a social network. Information propagation is modeled as a susceptible-infected (SI) process, and the campaign budget is fixed. Direct recruitment and word-of-mouth incentives are the two strategies to accelerate information spreading (controls). We allow for multiple controls depending on the degree of the nodes/individuals. The solution optimally allocates the scarce resource over the campaign duration and the degree class groups. We study the impact of the degree distribution of the network on the controls and present results for Erdős-Renyi and scale-free networks. Results show that more resource is allocated to high-degree nodes in the case of scale-free networks, but medium-degree nodes in the case of Erdős-Renyi networks. We study the effects of various model parameters on the optimal strategy and quantify the improvement offered by the optimal strategy over the static and bang-bang control strategies. The effect of the time-varying spreading rate on the controls is explored as the interest level of the population in the subject of the campaign may change over time. We show the existence of a solution to the formulated optimal control problem, which has nonlinear isoperimetric constraints, using novel techniques that is general and can be used in other similar optimal control problems. This work may be of interest to political, social awareness, or crowdfunding campaigners and product marketing managers, and with some modifications may be used for mitigating biological epidemics.

Connected through hyperedge HE-1 (induced by source paper "transient dynamics of epidemic spreading and its mitigation on large networks"):
  with members:
    - "on the dynamics of deterministic epidemic propagation over networks"
    - "analysis of exact and approximated epidemic models over complex networks"
    - "optimal patching in clustered malware epidemics"
    - "optimal energy aware epidemic routing in dtns"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"

Connected through hyperedge HE-2 (induced by source paper "multistage campaigning in social networks"):
  with members:
    - "steering opinion dynamics in information diffusion networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"
    - "modeling opinion dynamics in diffusion networks"
    - "influence prediction for continuous time information propagation on networks"
    - "learning and forecasting opinion dynamics in social networks"
    - "shaping social activity by incentivizing users"
    - "modeling adoption and usage of competing products"

Connected through hyperedge HE-3 (induced by source paper "incentivized campaigning in social networks"):
  with members:
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "how to run a campaign optimal control of sis and sir information epidemics"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-4 (induced by source paper "cost effective campaigning in social networks"):
  with members:
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "how to run a campaign optimal control of sis and sir information epidemics"

Connected through hyperedge HE-5 (induced by source paper "characterization and control of diffusion processes in multi agent networks"):
  with members:
    - "distributed pareto optimization via diffusion strategies"
    - "opinion fluctuations and disagreement in social networks"
    - "robust distributed routing in dynamical flow networks"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "measuring transport difficulty of data dissemination in large scale online social networks an interest driven case"):
  with members:
    - "conjoining speeds up information diffusion in overlaying social physical networks"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "the role of social networks in information diffusion"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc 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)