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: "statistical model checking an overview"
  Abstract: Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to \emph{simulate} the system for finitely many runs, and use \emph{hypothesis testing} to infer whether the samples provide a \emph{statistical} evidence for the satisfaction or violation of the specification. In this short paper, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity.

Connected through hyperedge HE-1 (induced by source paper "probabilistic verification of fairness properties via concentration"):
  with members:
    - "static analysis for probabilistic programs"
    - "fairness with dynamics"
    - "certified defenses against adversarial examples"
    - "fairness testing testing software for discrimination"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "algorithmic decision making and the cost of fairness"
    - "a neural representation of sketch drawings"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-2 (induced by source paper "statistical epistemic logic"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "hybrid statistical estimation of mutual information and its application to information flow"
    - "towards logical specification of statistical machine learning"
    - "hyperpctl a temporal logic for probabilistic hyperproperties"
    - "on the anonymization of differentially private location obfuscation"
    - "proving differential privacy in hoare logic"
    - "an epistemic approach to coercion resistance for electronic voting protocols"
    - "anonymity and information hiding in multiagent systems"

Connected through hyperedge HE-3 (induced by source paper "safety analysis for vehicle guidance systems with dynamic fault trees"):
  with members:
    - "one net fits all a unifying semantics of dynamic fault trees using gspns"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "quantum quantitative safety analysis of uml models"

Connected through hyperedge HE-4 (induced by source paper "activforms a model based approach to engineer self adaptive systems"):
  with members:
    - "perpetual assurances for self adaptive systems"
    - "using models at runtime to address assurance for self adaptive systems"
    - "model driven engineering of self adaptive software with eurema"

Connected through hyperedge HE-5 (induced by source paper "mcpa program analysis as machine learning"):
  with members:
    - "assurances in software testing a roadmap"
    - "approximate counting in smt and value estimation for probabilistic programs"

Connected through hyperedge HE-6 (induced by source paper "perpetual assurances for self adaptive systems"):
  with members:
    - "using models at runtime to address assurance for self adaptive systems"
    - "model driven engineering of self adaptive software with eurema"

Connected through hyperedge HE-7 (induced by source paper "evaluating model testing and model checking for finding requirements violations in simulink models"):
  with members:
    - "generating automated and online test oracles for simulink models with continuous and uncertain behaviors"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "verifying fairness properties via concentration"):
  with members:
    - "certified defenses against adversarial examples"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "algorithmic decision making and the cost of fairness"
    - "a neural representation of sketch drawings"
    - "equality of opportunity in supervised learning"
    - "measuring neural net robustness with constraints"
    - "fairness through awareness"
    - "an abstract monte carlo method for the analysis of probabilistic programs"

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)