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: "efficient checking of individual rewards properties in markov population models"
  Abstract: In recent years fluid approaches to the analysis of Markov populations models have been demonstrated to have great pragmatic value. Initially developed to estimate the behaviour of the system in terms of the expected values of population counts, the fluid approach has subsequently been extended to more sophisticated interrogations of models through its embedding within model checking procedures. In this paper we extend recent work on checking CSL properties of individual agents within a Markovian population model, to consider the checking of properties which incorporate rewards.

Connected through hyperedge HE-1 (induced by source paper "quantitative aspects of programming languages and systems over the past 2 4 years and beyond"):
  with members:
    - "paws a tool for the analysis of weighted systems"
    - "divide and conquer variable set separation in hybrid systems reachability analysis"
    - "logical characterization of trace metrics"
    - "design and optimisation of the flyfast front end for attribute based coordination"
    - "language based abstractions for dynamical systems"
    - "limit your consumption finding bounds in average energy games"
    - "mela modelling in ecology with location attributes"
    - "logical characterization of bisimulation metrics"

Connected through hyperedge HE-2 (induced by source paper "central limit model checking"):
  with members:
    - "probabilistic model checking for continuous time markov chains via sequential bayesian inference"
    - "programming discrete distributions with chemical reaction networks"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "fluid model checking"
    - "hybrid behaviour of markov population models"

Connected through hyperedge HE-3 (induced by source paper "model checking markov population models by stochastic approximations"):
  with members:
    - "probabilistic model checking for continuous time markov chains via sequential bayesian inference"
    - "fluid model checking of timed properties"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "fluid model checking"
    - "model checking of continuous time markov chains against timed automata specifications"

Connected through hyperedge HE-4 (induced by source paper "the power of synchronisation formal analysis of power consumption in networks of pulse coupled oscillators"):
  with members:
    - "a review of automated formal verification of ad hoc routing protocols for wireless sensor networks"

Connected through hyperedge HE-5 (induced by source paper "fluid model checking of timed properties"):
  with members:
    - "approximating acceptance probabilities of ctmc paths on multi clock deterministic timed automata"
    - "model checking of continuous time markov chains against timed automata specifications"

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.LO(Logic in Computer Science)