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 abstractions of interconnected discrete time stochastic control systems"
  Abstract: This paper is concerned with a compositional approach for constructing abstractions of interconnected discrete-time stochastic control systems. The 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 their abstractions in the probabilistic setting. Accordingly, one can leverage the proposed results to perform analysis and synthesis over abstract interconnected systems, and then carry the results over concrete ones. In the first part of the paper, we derive sufficient small-gain type conditions for the compositional quantification of the distance in probability between the interconnection of stochastic control subsystems and that of their abstractions. In the second part of the paper, we focus on the class of discrete-time linear stochastic control systems with independent noises in the abstract and concrete subsystems. For this class of systems, we propose a computational scheme to construct abstractions together with their corresponding stochastic simulation functions. We demonstrate the effectiveness of the proposed results by constructing an abstraction (totally 4 dimensions) of the interconnection of four discrete-time linear stochastic control subsystems (together 100 dimensions) in a compositional fashion.

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 construction of infinite abstractions for networks of 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 construction of infinite abstractions for networks of 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 "compositional construction of infinite abstractions for networks of stochastic control systems"):
  with members:
    - "robust dynamic programming for temporal logic control of stochastic systems"
    - "compositional in finite abstractions for large scale interconnected stochastic systems"
    - "compositional construction of finite state abstractions for stochastic control systems"
    - "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-4 (induced by source paper "from dissipativity theory to compositional construction of finite markov decision processes"):
  with members:
    - "compositional construction of infinite abstractions for networks of 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)