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: "mean field for markov decision processes from discrete to continuous optimization"
  Abstract: We study the convergence of Markov Decision Processes made of a large number of objects to optimization problems on ordinary differential equations (ODE). We show that the optimal reward of such a Markov Decision Process, satisfying a Bellman equation, converges to the solution of a continuous Hamilton-Jacobi-Bellman (HJB) equation based on the mean field approximation of the Markov Decision Process. We give bounds on the difference of the rewards, and a constructive algorithm for deriving an approximating solution to the Markov Decision Process from a solution of the HJB equations. We illustrate the method on three examples pertaining respectively to investment strategies, population dynamics control and scheduling in queues are developed. They are used to illustrate and justify the construction of the controlled ODE and to show the gain obtained by solving a continuous HJB equation rather than a large discrete Bellman equation.

Connected through hyperedge HE-1 (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"
    - "how to run a campaign optimal control of sis and sir information epidemics"

Connected through hyperedge HE-2 (induced by source paper "groups of repairmen and repair based load balancing in supermarket models with repairable servers"):
  with members:
    - "nonlinear markov processes in big networks"
    - "block structured supermarket models"

Connected through hyperedge HE-3 (induced by source paper "the complexity of synchronizing markov decision processes"):
  with members:
    - "improving the upper bound on the length of the shortest reset word"
    - "controlling a population"
    - "synchronizing data words for register automata"
    - "computation tree logic for synchronization properties"
    - "trace refinement in labelled markov decision processes"
    - "synchronizing weighted automata"
    - "probabilistic bisimulation naturally on distributions"
    - "fast synchronization of random automata"

Connected through hyperedge HE-4 (induced by source paper "optimal patching in clustered malware epidemics"):
  with members:
    - "optimal control in two hop relay routing"

Connected through hyperedge HE-5 (induced by source paper "nonlinear markov processes in big networks"):
  with members:
    - "block structured supermarket models"
    - "analysis of loss networks with routing"

Connected through hyperedge HE-6 (induced by source paper "probabilistic bisimulation naturally on distributions"):
  with members:
    - "robust synchronization in markov decision processes"
    - "when equivalence and bisimulation join forces in probabilistic automata"
    - "bisimulations meet pctl equivalences for probabilistic automata"
    - "late weak bisimulation for markov automata"
    - "deciding probabilistic automata weak bisimulation in polynomial time"
    - "qualitative analysis of partially observable markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "passive dynamics in mean field control"):
  with members:
    - "electrical vehicles in the smart grid a mean field game analysis"
    - "universal and composite hypothesis testing via mismatched divergence"

Connected through hyperedge HE-8 (induced by source paper "spread of influence and content in mobile opportunistic networks"):
  with members:
    - "co evolution of content popularity and delivery in mobile p2p networks"
    - "optimal forwarding in delay tolerant networks with multiple destinations"

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.AI(Artificial Intelligence)