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: "compositional construction of infinite abstractions for networks of stochastic control systems"
  Abstract: This paper is concerned with a compositional approach for constructing infinite abstractions of interconnected discrete-time stochastic control systems. The proposed approach uses the interconnection matrix and joint dissipativity-type properties of subsystems and their abstractions described by a new notion of so-called stochastic storage functions. The interconnected abstraction framework is based on new notions of so-called stochastic simulation functions, using which one can quantify the distance between original interconnected stochastic control systems and interconnected abstractions in the probabilistic setting. In the first part of the paper, we derive dissipativity-type compositional reasoning for the quantification of the distance in probability between the interconnection of stochastic control subsystems and that of their abstractions. Moreover, we consider specifications expressed as syntactically co-safe linear temporal logic formulae and show how a synthesized policy for the abstract system can be refined to a policy for the original system while providing guarantee on the probability of satisfaction. In the second part of the paper, we focus on a class of discrete-time nonlinear stochastic control systems with independent noises in the abstract and concrete subsystems, and propose a computational scheme to construct abstractions together with their corresponding stochastic storage functions. We demonstrate the effectiveness of the proposed results by constructing an abstraction (totally 3 dimensions) of the interconnection of three discrete-time nonlinear stochastic control subsystems (together 222 dimensions) in a compositional fashion. We also employ the abstraction as a substitute to synthesize a controller enforcing a syntactically co-safe linear temporal logic specification.

Connected through hyperedge HE-1 (induced by source paper "compositional abstraction based synthesis of general mdps via approximate probabilistic relations"):
  with members:
    - "temporal logic control of general markov decision processes by approximate policy refinement"
    - "robust dynamic programming for temporal logic control of stochastic 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"

Connected through hyperedge HE-2 (induced by source paper "compositional in finite abstractions for large scale interconnected stochastic systems"):
  with members:
    - "compositional abstraction based synthesis of general mdps via approximate probabilistic relations"
    - "compositional abstractions of interconnected discrete time stochastic control systems"
    - "from dissipativity theory to compositional synthesis of symbolic models"
    - "compositional abstraction and safety synthesis using overlapping symbolic models"
    - "from dissipativity theory to compositional construction of finite markov decision processes"
    - "dynamic bayesian networks as formal abstractions of structured stochastic processes"

Connected through hyperedge HE-3 (induced by source paper "from dissipativity theory to compositional construction of finite markov decision processes"):
  with members:
    - "compositional abstractions of interconnected discrete time stochastic control systems"
    - "compositional construction of finite state abstractions for stochastic control systems"
    - "compositional abstraction and safety synthesis using overlapping symbolic models"
    - "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)