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: "on the robustness of temporal properties for stochastic models"
  Abstract: Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic Hybrid Automata (SHA) are powerful formalisms to model and to reason about the dynamics of biological systems, due to their ability to capture the stochasticity inherent in biological processes. A classical question in formal modelling with clear relevance to biological modelling is the model checking problem, i.e. calculate the probability that a behaviour, expressed for instance in terms of a certain temporal logic formula, may occur in a given stochastic process. However, one may not only be interested in the notion of satisfiability, but also in the capacity of a system to mantain a particular emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic noise, or by possible small changes in the model parameters. To address this issue, researchers from the verification community have recently proposed several notions of robustness for temporal logic providing suitable definitions of distance between a trajectory of a (deterministic) dynamical system and the boundaries of the set of trajectories satisfying the property of interest. The contributions of this paper are twofold. First, we extend the notion of robustness to stochastic systems, showing that this naturally leads to a distribution of robustness scores. By discussing two examples, we show how to approximate the distribution of the robustness score and its key indicators: the average robustness and the conditional average robustness. Secondly, we show how to combine these indicators with the satisfaction probability to address the system design problem, where the goal is to optimize some control parameters of a stochastic model in order to best maximize robustness of the desired specifications.

Connected through hyperedge HE-1 (induced by source paper "falsification of cyber physical systems using deep reinforcement learning"):
  with members:
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "an active learning approach to the falsification of black box cyber physical systems"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "reinforcement learning with temporal logic rewards"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "mdp optimal control under temporal logic constraints"

Connected through hyperedge HE-2 (induced by source paper "how to learn a model checker"):
  with members:
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "output reachable set estimation and verification for multi layer neural networks"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "smoothed model checking for uncertain continuous time markov chains"

Connected through hyperedge HE-3 (induced by source paper "statistical model checking for biological applications"):
  with members:
    - "delta decidability over the reals"
    - "uppaal smc statistical model checking for priced timed automata"

Connected through hyperedge HE-4 (induced by source paper "learning and designing stochastic processes from logical constraints"):
  with members:
    - "smoothed model checking for uncertain continuous time markov chains"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-5 (induced by source paper "semiring based specification approaches for quantitative security"):
  with members:
    - "weak bisimulations for labelled transition systems weighted over semirings"

Connected through hyperedge HE-6 (induced by source paper "smoothed model checking for uncertain continuous time markov chains"):
  with members:
    - "learning and designing stochastic processes from logical constraints"
    - "learning temporal logical properties discriminating ecg models of cardiac arrhytmias"
    - "expectation propagation for approximate bayesian inference"
    - "hybrid performance modelling of opportunistic networks"
    - "cross entropy optimisation of importance sampling parameters for statistical model checking"

Connected through hyperedge HE-7 (induced by source paper "on robustness analysis of stochastic biochemical systems by probabilistic model checking"):
  with members:
    - "fluid model checking"

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)