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: "mining parametric temporal logic properties in model based design for cyber physical systems"
  Abstract: One of the advantages of adopting a model-based development process is that it enables testing and verification at early stages of development. However, it is often desirable to not only verify/falsify certain formal system specifications, but also to automatically explore the properties that the system satisfies. In this work, we present a framework that enables property exploration for cyber-physical systems. Namely, given a parametric specification with multiple parameters, our solution can automatically infer the ranges of parameters for which the property does not hold on the system. In this paper, we consider parametric specifications in metric or Signal Temporal Logic (MTL or STL). Using robust semantics for MTL, the parameter mining problem can be converted into a Pareto optimization problem for which we can provide an approximate solution by utilizing stochastic optimization methods. We include algorithms for the exploration and visualization of multi-parametric specifications. The framework is demonstrated on an industrial size, high-fidelity engine model as well as examples from related literature.

Connected through hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "information guided temporal logic inference with prior knowledge"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "teaching categories to human learners with visual explanations"
    - "learning linear temporal properties"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "learning task specifications from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "transfer of temporal logic formulas in reinforcement learning"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"
    - "safe reinforcement learning via shielding"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-3 (induced by source paper "graph temporal logic inference for classification and identification"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"
    - "the robotarium a remotely accessible swarm robotics research testbed"

Connected through hyperedge HE-4 (induced by source paper "a data driven falsification based model of human driver behavior"):
  with members:
    - "longitudinal trajectory prediction of human driven vehicles near traffic lights"
    - "using control synthesis to generate corner cases a case study on autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "metrics for signal temporal logic formulae"):
  with members:
    - "diagnosis and repair for synthesis from signal temporal logic specifications"
    - "time window temporal logic"
    - "on the minimal revision problem of specification automata"
    - "on line monitoring for temporal logic robustness"

Connected through hyperedge HE-6 (induced by source paper "a robust genetic algorithm for learning temporal specifications from data"):
  with members:
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

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)