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: "temporal logic control of general markov decision processes by approximate policy refinement"
  Abstract: The formal verification and controller synthesis for general Markov decision processes (gMDPs) that evolve over uncountable state spaces are computationally hard and thus generally rely on the use of approximate abstractions. In this paper, we contribute to the state of the art of control synthesis for temporal logic properties by computing and quantifying a less conservative gridding of the continuous state space of linear stochastic dynamic systems and by giving a new approach for control synthesis and verification that is robust to the incurred approximation errors. The approximation errors are expressed as both deviations in the outputs of the gMDPs and in the probabilistic transitions.

Connected through hyperedge HE-1 (induced by source paper "constraint satisfaction propagation non stationary policy synthesis for temporal logic planning"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "rudder return decomposition for delayed rewards"
    - "variational option discovery algorithms"
    - "eigenoption discovery through the deep successor representation"
    - "the eigenoption critic framework"
    - "hierarchical subtask discovery with non negative matrix factorization"
    - "environment independent task specifications via gltl"
    - "the option critic architecture"

Connected through hyperedge HE-2 (induced by source paper "compositional abstraction based synthesis of general mdps via approximate probabilistic relations"):
  with members:
    - "robust dynamic programming for temporal logic control of stochastic systems"
    - "compositional construction of infinite abstractions for networks of stochastic control systems"
    - "compositional abstractions of interconnected discrete time stochastic control systems"
    - "from dissipativity theory to compositional construction of finite markov decision processes"
    - "compositional synthesis of finite state abstractions"
    - "dynamic bayesian networks as formal abstractions of structured stochastic processes"

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)