human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "strategy representation by decision trees with linear classifiers"):
  Abstract: Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of $\omega$-regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategies, and we consider succinct representation of such strategies. The decision-tree data structure from machine learning retains the flavor of decisions of strategies and allows entropy-based minimization to obtain succinct trees. However, in contrast to traditional machine-learning problems where small errors are allowed, for winning strategies in graph games and MDPs no error is allowed, and the decision tree must represent the entire strategy. In this work we propose decision trees with linear classifiers for representation of strategies in graph games and MDPs. We have implemented strategy representation using this data structure and we present experimental results for problems on graph games and MDPs, which show that this new data structure presents a much more efficient strategy representation as compared to standard decision trees.

Includes members:
  Member "strategy representation by decision trees in reactive synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dtcontrol decision tree learning algorithms for controller representation")
  Member "a storm is coming a modern probabilistic model checker":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the probabilistic model checker storm")
  Member "an automaton learning approach to solving safety games over infinite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimally resilient strategies in pushdown safety games")
  Member "the second reactive synthesis competition syntcomp 2015":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "functional synthesis via input output separation")
  Member "counterexample explanation by learning small strategies in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dtcontrol decision tree learning algorithms for controller representation")
  Member "extended aiger format for synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the extended hoa format for synthesis")
  Member "measuring and synthesizing systems in probabilistic environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantitative automata under probabilistic semantics")

Question: Please tell me which class the source paper 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)