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: "persistent monitoring in discrete environments minimizing the maximum weighted latency between observations"
  Abstract: In this paper, we consider the problem of planning a path for a robot to monitor a known set of features of interest in an environment. We represent the environment as a graph with vertex weights and edge lengths. The vertices represent regions of interest, edge lengths give travel times between regions, and the vertex weights give the importance of each region. As the robot repeatedly performs a closed walk on the graph, we define the weighted latency of a vertex to be the maximum time between visits to that vertex, weighted by the importance (vertex weight) of that vertex. Our goal is to find a closed walk that minimizes the maximum weighted latency of any vertex. We show that there does not exist a polynomial time algorithm for the problem. We then provide two approximation algorithms; an $O(\log n)$-approximation algorithm and an $O(\log \rho_G)$-approximation algorithm, where $\rho_G$ is the ratio between the maximum and minimum vertex weights. We provide simulation results which demonstrate that our algorithms can be applied to problems consisting of thousands of vertices, and a case study for patrolling a city for crime.

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"
    - "an optimal control approach for the persistent monitoring problem"

Connected through hyperedge HE-2 (induced by source paper "multi robot routing for persistent monitoring with latency constraints"):
  with members:
    - "submodular trajectory optimization for aerial 3d scanning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"

Connected through hyperedge HE-3 (induced by source paper "persistent monitoring of dynamically changing environments using an unmanned vehicle"):
  with members:
    - "persistent robotic tasks monitoring and sweeping in changing environments"

Connected through hyperedge HE-4 (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 robotic tasks monitoring and sweeping in changing environments"
    - "an optimal control approach for the persistent monitoring problem"

Connected through hyperedge HE-5 (induced by source paper "algorithms for visibility based monitoring with robot teams"):
  with members:
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "persistent robotic tasks monitoring and sweeping in changing environments"

Connected through hyperedge HE-6 (induced by source paper "an optimal control approach for the data harvesting problem"):
  with members:
    - "decentralized goal assignment and trajectory generation in multi robot networks"

Connected through hyperedge HE-7 (induced by source paper "correlated orienteering problem and it application to persistent monitoring tasks"):
  with members:
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "memoryless control design for persistent surveillance under safety constraints"
    - "persistent robotic tasks monitoring and sweeping in changing environments"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "optimal tourist problem and anytime planning of trip itineraries"):
  with members:
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "persistent robotic tasks monitoring and sweeping in changing environments"
    - "sampling based algorithms for optimal motion planning"

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.DS(Data Structures and Algorithms)