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: "fluid model checking"
  Abstract: In this paper we investigate a potential use of fluid approximation techniques in the context of stochastic model checking of CSL formulae. We focus on properties describing the behaviour of a single agent in a (large) population of agents, exploiting a limit result known also as fast simulation. In particular, we will approximate the behaviour of a single agent with a time-inhomogeneous CTMC which depends on the environment and on the other agents only through the solution of the fluid differential equation. We will prove the asymptotic correctness of our approach in terms of satisfiability of CSL formulae and of reachability probabilities. We will also present a procedure to model check time-inhomogeneous CTMC against CSL formulae.

Connected through hyperedge HE-1 (induced by source paper "semi quantitative abstraction and analysis of chemical reaction networks"):
  with members:
    - "model checking gene regulatory networks"
    - "two domain dna strand displacement"
    - "the computational power of population protocols"

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"
    - "efficient checking of individual rewards properties in markov population models"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "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"
    - "efficient checking of individual rewards properties in markov population models"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "model checking of continuous time markov chains against timed automata specifications"

Connected through hyperedge HE-4 (induced by source paper "probabilistic model checking for continuous time markov chains via sequential bayesian inference"):
  with members:
    - "model checking markov population models by stochastic approximations"
    - "smoothed model checking for uncertain continuous time markov chains"

Connected through hyperedge HE-5 (induced by source paper "design and optimisation of the flyfast front end for attribute based coordination"):
  with members:
    - "on the fly probabilistic model checking"

Connected through hyperedge HE-6 (induced by source paper "from collective adaptive systems to human centric computation and back spatial model checking for medical imaging"):
  with members:
    - "a formal methods approach to pattern synthesis in reaction diffusion systems"

Connected through hyperedge HE-7 (induced by source paper "efficient checking of individual rewards properties in markov population models"):
  with members:
    - "hybrid performance modelling of opportunistic networks"

Connected through hyperedge HE-8 (induced by source paper "stochastically timed predicate based communication primitives for autonomic computing"):
  with members:
    - "a semi quantitative equivalence for abstracting from fast reactions"
    - "comparing the expressive power of the synchronous and asynchronous pi calculi"

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)