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: "counterexample explanation by learning small strategies in markov decision processes"
  Abstract: While for deterministic systems, a counterexample to a property can simply be an error trace, counterexamples in probabilistic systems are necessarily more complex. For instance, a set of erroneous traces with a sufficient cumulative probability mass can be used. Since these are too large objects to understand and manipulate, compact representations such as subchains have been considered. In the case of probabilistic systems with non-determinism, the situation is even more complex. While a subchain for a given strategy (or scheduler, resolving non-determinism) is a straightforward choice, we take a different approach. Instead, we focus on the strategy - which can be a counterexample to violation of or a witness of satisfaction of a property - itself, and extract the most important decisions it makes, and present its succinct representation. The key tools we employ to achieve this are (1) introducing a concept of importance of a state w.r.t. the strategy, and (2) learning using decision trees. There are three main consequent advantages of our approach. Firstly, it exploits the quantitative information on states, stressing the more important decisions. Secondly, it leads to a greater variability and degree of freedom in representing the strategies. Thirdly, the representation uses a self-explanatory data structure. In summary, our approach produces more succinct and more explainable strategies, as opposed to e.g. binary decision diagrams. Finally, our experimental results show that we can extract several rules describing the strategy even for very large systems that do not fit in memory, and based on the rules explain the erroneous behaviour.

Connected through hyperedge HE-1 (induced by source paper "dtcontrol decision tree learning algorithms for controller representation"):
  with members:
    - "compositional synthesis of symbolic models for networks of switched systems"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "strategy representation by decision trees with linear classifiers"
    - "strategy representation by decision trees in reactive synthesis"
    - "optimal symbolic controllers determinization for bdd storage"
    - "software fault tolerance for cyber physical systems via full system restart"
    - "invariance feedback entropy of uncertain control systems"
    - "quantitative implementation strategies for safety controllers"

Connected through hyperedge HE-2 (induced by source paper "the challenges in specifying and explaining synthesized implementations of reactive systems"):
  with members:
    - "bosy an experimentation framework for bounded synthesis"
    - "synthesizing skeletons for reactive systems"
    - "strategy representation by decision trees in reactive synthesis"
    - "reactive synthesis towards output sensitive algorithms"
    - "the 4th reactive synthesis competition syntcomp 2017 benchmarks participants results"
    - "bounded cycle synthesis"
    - "structural synthesis for gxw specifications"
    - "detecting unrealizability of distributed fault tolerant systems"

Connected through hyperedge HE-3 (induced by source paper "strategy representation by decision trees with linear classifiers"):
  with members:
    - "strategy representation by decision trees in reactive synthesis"
    - "a storm is coming a modern probabilistic model checker"
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "the second reactive synthesis competition syntcomp 2015"
    - "extended aiger format for synthesis"
    - "measuring and synthesizing systems in probabilistic environments"

Connected through hyperedge HE-4 (induced by source paper "farkas certificates and minimal witnesses for probabilistic reachability constraints"):
  with members:
    - "verification of markov decision processes using learning algorithms"
    - "cegar for qualitative analysis of probabilistic systems"
    - "multi objective model checking of markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "strategy representation by decision trees in reactive synthesis"):
  with members:
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "the second reactive synthesis competition syntcomp 2015"
    - "learning invariants using decision trees"
    - "extended aiger format for synthesis"

Connected through hyperedge HE-6 (induced by source paper "stochastic shortest path with energy constraints in pomdps"):
  with members:
    - "optimal cost almost sure reachability in pomdps"
    - "solving pomdps by searching the space of finite policies"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "heuristic search value iteration for pomdps"
    - "energy and mean payoff parity markov decision processes"
    - "qualitative analysis of partially observable markov decision processes"

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)