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: "how to run a campaign optimal control of sis and sir information epidemics"
  Abstract: Information spreading in a population can be modeled as an epidemic. Campaigners (e.g., election campaign managers, companies marketing products or movies) are interested in spreading a message by a given deadline, using limited resources. In this paper, we formulate the above situation as an optimal control problem and the solution (using Pontryagin's Maximum Principle) prescribes an optimal resource allocation over the time of the campaign. We consider two different scenarios-in the first, the campaigner can adjust a direct control (over time) which allows her to recruit individuals from the population (at some cost) to act as spreaders for the Susceptible-Infected-Susceptible (SIS) epidemic model. In the second case, we allow the campaigner to adjust the effective spreading rate by incentivizing the infected in the Susceptible-Infected-Recovered (SIR) model, in addition to the direct recruitment. We consider time varying information spreading rate in our formulation to model the changing interest level of individuals in the campaign, as the deadline is reached. In both the cases, we show the existence of a solution and its uniqueness for sufficiently small campaign deadlines. For the fixed spreading rate, we show the effectiveness of the optimal control strategy against the constant control strategy, a heuristic control strategy and no control. We show the sensitivity of the optimal control to the spreading rate profile when it is time varying.

Connected through hyperedge HE-1 (induced by source paper "influencing opinion dynamics in networks with limited interaction"):
  with members:
    - "influencing opinions of heterogeneous populations over finite time horizons"
    - "majority model on random regular graphs"
    - "incentivized campaigning in social networks"

Connected through hyperedge HE-2 (induced by source paper "maintaining ferment on opinion control over social networks"):
  with members:
    - "discrete time optimal control with frequency constraints for non smooth systems"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "opinion fluctuations and disagreement in social networks"

Connected through hyperedge HE-3 (induced by source paper "influencing opinions of heterogeneous populations over finite time horizons"):
  with members:
    - "incentivized campaigning in social networks"

Connected through hyperedge HE-4 (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"
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"

Connected through hyperedge HE-5 (induced by source paper "visibility aware optimal contagion of malware epidemics"):
  with members:
    - "optimal patching in clustered malware epidemics"
    - "optimal energy aware epidemic routing in dtns"
    - "mean field for markov decision processes from discrete to continuous optimization"

Connected through hyperedge HE-6 (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"
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "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-7 (induced by source paper "incentivized campaigning in social networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-8 (induced by source paper "cost effective campaigning in social networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "optimal control of information epidemics modeled as maki thompson rumors"

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)