human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a sub modular receding horizon solution for mobile multi agent persistent monitoring"):
  Abstract: Author(s): Rezazadeh, Navid; Kia, Solmaz S | Abstract: We study the problem of persistent monitoring of finite number of inter-connected geographical nodes for event detection via a group of heterogeneous mobile agents. We use Poisson process model to capture the probability of the events occurring at the geographical nodes. We then tie a utility function to the probability of detecting an event in each point of interest and use it in our policy design to incentivize the agents to visit the geographical nodes with higher probability of event occurrence. We show that the design of an optimal monitoring policy to maximize the utility of event detection over a mission horizon is an NP-hard problem. By showing that the reward function is a monotone increasing and submodular function, we then proceed to propose a suboptimal dispatch policy design with a known optimality gap. To reduce the time complexity of constructing the feasible search set and also to induce robustness to changes in event occurrence and other operational factors, we preform our suboptimal policy design in a receding horizon setting. Our next contribution is to add a new term to our optimization problem to compensate for the shortsightedness of the receding horizon approach. This added term provides a measure of importance for nodes beyond the receding horizon's sight, and is meant to give the policy design an intuition to steer the agents towards areas with higher importance on the global map. Finally, we discuss how our proposed algorithm can be implemented in a decentralized manner. We demonstrate our results through a simulation study.

Includes members:
  Member "multi robot routing for persistent monitoring with latency constraints":
    also connected through other hyperedges:
      (none)
  Member "combinatorial algorithms for control of biological regulatory networks":
    also connected through other hyperedges:
      (none)
  Member "distributed submodular maximization with limited information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed submodular maximization with parallel execution")
  Member "persistent monitoring of events with stochastic arrivals at multiple stations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring")
  Member "a supermodular optimization framework for leader selection under link noise in linear multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "current flow group closeness centrality for complex networks")

Question: Please tell me which class the source paper 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.MA(Multiagent Systems)