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: "significant diagnostic counterexamples in probabilistic model checking"
  Abstract: This paper presents a novel technique for counterexample generation in probabilistic model checking of Markov Chains and Markov Decision Processes. (Finite) paths in counterexamples are grouped together in witnesses that are likely to provide similar debugging information to the user. We list five properties that witnesses should satisfy in order to be useful as debugging aid: similarity, accuracy, originality, significance, and finiteness. Our witnesses contain paths that behave similar outside strongly connected components. #R##N#This papers shows how to compute these witnesses by reducing the problem of generating counterexamples for general properties over Markov Decision Processes, in several steps, to the easy problem of generating counterexamples for reachability properties over acyclic Markov Chains.

Connected through hyperedge HE-1 (induced by source paper "verifying reachability properties in markov chains via incremental induction"):
  with members:
    - "ic3 modulo theories via implicit predicate abstraction"

Connected through hyperedge HE-2 (induced by source paper "counterexample explanation by learning small strategies in markov decision processes"):
  with members:
    - "high level counterexamples for probabilistic automata"
    - "permissive controller synthesis for probabilistic systems"
    - "verification of markov decision processes using learning algorithms"
    - "cegar for qualitative analysis of probabilistic systems"
    - "spudd stochastic planning using decision diagrams"
    - "assume guarantee abstraction refinement for probabilistic systems"
    - "measuring permissiveness in parity games mean payoff parity games revisited"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-3 (induced by source paper "high level counterexamples for probabilistic automata"):
  with members:
    - "assume guarantee abstraction refinement for probabilistic systems"

Connected through hyperedge HE-4 (induced by source paper "refinement and difference for probabilistic automata"):
  with members:
    - "assume guarantee abstraction refinement for probabilistic systems"

Connected through hyperedge HE-5 (induced by source paper "model exploration and analysis for quantitative safety refinement in probabilistic b"):
  with members:
    - "probabilistic model based safety analysis"
    - "quantitative safety linking proof based verification with model checking for probabilistic systems"

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)