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: "optimal control of information epidemics modeled as maki thompson rumors"
  Abstract: We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns. (C) 2014 Elsevier B.V. All rights reserved.

Connected through hyperedge HE-1 (induced by source paper "using node centrality and optimal control to maximize information diffusion in social networks"):
  with members:
    - "seed selection for spread of influence in social networks temporal vs static approach"
    - "how to run a campaign optimal control of sis and sir information epidemics"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

Connected through hyperedge HE-2 (induced by source paper "optimal resource allocation over time and degree classes for maximizing information dissemination in social networks"):
  with members:
    - "epidemic spreading with external agents"
    - "how to run a campaign optimal control of sis and sir information epidemics"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "epidemic thresholds with external agents"
    - "maximizing the spread of cascades using network design"

Connected through hyperedge HE-3 (induced by source paper "incentivized campaigning in social networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "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:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "how to run a campaign optimal control of sis and sir information epidemics"

Connected through hyperedge HE-5 (induced by source paper "campaigning in heterogeneous social networks optimal control of si information epidemics"):
  with members:
    - "how to run a campaign optimal control of sis and sir information epidemics"

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.SY(Systems and Control)