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 "compositional in finite abstractions for large scale interconnected stochastic systems"):
  Abstract: This paper is concerned with a compositional approach for constructing both infinite (reduced-order models) and finite abstractions (a.k.a. finite Markov decision processes (MDPs)) of large-scale interconnected discrete-time stochastic systems. The proposed framework is based on the notion of stochastic simulation functions enabling us to employ an abstract system as a substitution of the original one in the controller design process with guaranteed error bounds. In the first part of the paper, we derive sufficient small-gain type conditions for the compositional quantification of the probabilistic distance between the interconnection of stochastic control subsystems and that of their infinite abstractions. We then construct infinite abstractions together with their corresponding stochastic simulation functions for a particular class of discrete-time nonlinear stochastic control systems. In the second part of the paper, we leverage small-gain type conditions for the compositional construction of finite abstractions. We propose an approach to construct finite MDPs as finite abstractions of concrete models or their reduced-order versions satisfying an incremental input-to-state stability property. We demonstrate the effectiveness of the proposed results by applying our approaches to a fully interconnected network of 20 nonlinear subsystems (totally 100 dimensions). We construct finite MDPs from their reduced-order versions (together 20 dimensions) with guaranteed error bounds on their output trajectories. We also apply the proposed results to a temperature regulation in a circular building and construct compositionally a finite abstraction of a network containing 1000 rooms. We employ the constructed finite abstractions as substitutes to compositionally synthesize policies regulating the temperature in each room for a bounded time horizon.

Includes members:
  Member "compositional abstraction based synthesis of general mdps via approximate probabilistic relations":
    also connected through other hyperedges:
      (none)
  Member "compositional construction of infinite abstractions for networks of stochastic control systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compositional abstraction based synthesis of general mdps via approximate probabilistic relations")
  Member "compositional abstractions of interconnected discrete time stochastic control systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compositional abstraction based synthesis of general mdps via approximate probabilistic relations")
  Member "from dissipativity theory to compositional synthesis of symbolic models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on approximate opacity of cyber physical systems")
  Member "compositional abstraction and safety synthesis using overlapping symbolic models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexible computational pipelines for robust abstraction based control synthesis")
  Member "from dissipativity theory to compositional construction of finite markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compositional abstraction based synthesis of general mdps via approximate probabilistic relations")
  Member "dynamic bayesian networks as formal abstractions of structured stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compositional abstraction based synthesis of general mdps via approximate probabilistic relations")

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.SY(Systems and Control)