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: "an optimal control approach for the persistent monitoring problem"
  Abstract: We propose an optimal control framework for persistent monitoring problems where the objective is to control the movement of mobile agents to minimize an uncertainty metric in a given mission space. For a single agent in a one-dimensional space, we show that the optimal solution is obtained in terms of a sequence of switching locations, thus reducing it to a parametric optimization problem. Using Infinitesimal Perturbation Analysis (IPA) we obtain a complete solution through a gradient-based algorithm. We also discuss a receding horizon controller which is capable of obtaining a near-optimal solution on-the-fly. We illustrate our approach with numerical examples.

Connected through hyperedge HE-1 (induced by source paper "age optimal information gathering and dissemination on graphs"):
  with members:
    - "age optimal trajectory planning for uav assisted data collection"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "update or wait how to keep your data fresh"
    - "persistent monitoring in discrete environments minimizing the maximum weighted latency between observations"

Connected through hyperedge HE-2 (induced by source paper "on sensing agility and computation requirements for a data gathering agile robotic vehicle"):
  with members:
    - "uncertainty in monotone co design problems"
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "persistent monitoring in discrete environments minimizing the maximum weighted latency between observations"
    - "persistent robotic tasks monitoring and sweeping in changing environments"

Connected through hyperedge HE-3 (induced by source paper "persistent monitoring in discrete environments minimizing the maximum weighted latency between observations"):
  with members:
    - "memoryless control design for persistent surveillance under safety constraints"
    - "persistent robotic tasks monitoring and sweeping in changing environments"
    - "capacitated vehicle routing with non uniform speeds"

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)