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: "strategy representation by decision trees in reactive synthesis"
  Abstract: Graph games played by two players over finite-state graphs are central in many problems in computer science. In particular, graph games with \omega-regular winning conditions, specified as parity objectives, which can express properties such as safety, liveness, fairness, are the basic framework for verification and synthesis of reactive systems. The decisions for a player at various states of the graph game are represented as strategies. While the algorithmic problem for solving graph games with parity objectives has been widely studied, the most prominent data-structure for strategy representation in graph games has been binary decision diagrams (BDDs). However, due to the bit-level representation, BDDs do not retain the inherent flavor of the decisions of strategies, and are notoriously hard to minimize to obtain succinct representation. In this work we propose decision trees for strategy representation in graph games. Decision trees retain the flavor of decisions of strategies and allow entropy-based minimization to obtain succinct trees. However, decision trees work in settings (e.g., probabilistic models) where errors are allowed, and overfitting of data is typically avoided. In contrast, for strategies in graph games no error is allowed, and the decision tree must represent the entire strategy. We develop new techniques to extend decision trees to overcome the above obstacles, while retaining the entropy-based techniques to obtain succinct trees. We have implemented our techniques to extend the existing decision tree solvers. We present experimental results for problems in reactive synthesis to show that decision trees provide a much more efficient data-structure for strategy representation as compared to BDDs.

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"
    - "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"
    - "counterexample explanation by learning small strategies in markov decision processes"

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"
    - "reactive synthesis towards output sensitive algorithms"
    - "the 4th reactive synthesis competition syntcomp 2017 benchmarks participants results"
    - "bounded cycle synthesis"
    - "structural synthesis for gxw specifications"
    - "counterexample explanation by learning small strategies in markov decision processes"
    - "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:
    - "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"
    - "counterexample explanation by learning small strategies in markov decision processes"
    - "extended aiger format for synthesis"
    - "measuring and synthesizing systems in probabilistic environments"

Connected through hyperedge HE-4 (induced by source paper "security aware synthesis using delayed action games"):
  with members:
    - "reactive control improvisation"
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "verification and control of partially observable probabilistic real time systems"
    - "integrating active sensing into reactive synthesis with temporal logic constraints under partial observations"
    - "delay games with wmso u winning conditions"

Connected through hyperedge HE-5 (induced by source paper "learning based synthesis of safety controllers"):
  with members:
    - "horn ice learning for synthesizing invariantsand contracts"
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "abstract learning frameworks for synthesis"

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)