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 energy aware epidemic routing in dtns"
  Abstract: In this work, we investigate the use of epidemic routing in energy constrained delay tolerant networks (DTNs). In epidemic routing, messages are relayed by intermediate nodes at contact opportunities, i.e., when pairs of nodes come within the transmission range of each other. Each node needs to decide whether to forward its message upon contact with a new node based on its own residual energy level and the age of that message. We mathematically characterize the fundamental trade-off between energy conservation and a measure of Quality of Service as a dynamic energy-dependent optimal control problem. We prove that in the mean-field regime, the optimal dynamic forwarding decisions follow simple threshold-based structures in which the forwarding threshold for each node depends on its current remaining energy. We then characterize the nature of this dependence. Our simulations reveal that the optimal dynamic policy significantly outperforms heuristics.

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"
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "analysis of exact and approximated epidemic models over complex networks"
    - "optimal patching in clustered malware epidemics"
    - "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 "visibility aware optimal contagion of malware epidemics"):
  with members:
    - "optimal patching in clustered malware epidemics"
    - "how to run a campaign optimal control of sis and sir information epidemics"
    - "mean field for markov decision processes from discrete to continuous optimization"

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)