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 abstraction based synthesis of general mdps via approximate probabilistic relations"):
  Abstract: We propose a compositional approach for constructing abstractions of general Markov decision processes using approximate probabilistic relations. The abstraction framework is based on the notion of $\delta$-lifted relations, using which one can quantify the distance in probability between the interconnected gMDPs and that of their abstractions. This new approximate relation unifies compositionality results in the literature by incorporating the dependencies between state transitions explicitly and by allowing abstract models to have either finite or infinite state spaces. Accordingly, one can leverage the proposed results to perform analysis and synthesis over abstract models, and then carry the results over concrete ones. To this end, we first propose our compositionality results using the new approximate probabilistic relation which is based on lifting. We then focus on a class of stochastic nonlinear dynamical systems and construct their abstractions using both model order reduction and space discretization in a unified framework. We provide conditions for simultaneous existence of relations incorporating the structure of the network. Finally, we demonstrate the effectiveness of the proposed results by considering a network of four nonlinear dynamical subsystems (together 12 dimensions) and constructing finite abstractions from their reduced-order versions (together 4 dimensions) in a unified compositional framework. We benchmark our results against the compositional abstraction techniques that construct both infinite abstractions (reduced-order models) and finite MDPs in two consecutive steps. We show that our approach is much less conservative than the ones available in the literature.

Includes members:
  Member "temporal logic control of general markov decision processes by approximate policy refinement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constraint satisfaction propagation non stationary policy synthesis for temporal logic planning")
  Member "robust dynamic programming for temporal logic control of stochastic systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compositional construction of infinite abstractions for networks of stochastic control systems")
  Member "compositional construction of infinite abstractions for networks of stochastic control systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compositional in finite abstractions for large scale interconnected stochastic systems")
  Member "compositional abstractions of interconnected discrete time stochastic control systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compositional in finite abstractions for large scale interconnected stochastic systems")
  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 in finite abstractions for large scale interconnected stochastic systems")
  Member "compositional synthesis of finite state abstractions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "synthesizing functional reactive programs")
  Member "dynamic bayesian networks as formal abstractions of structured stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compositional in finite abstractions for large scale interconnected stochastic systems")

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)