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 "fatal attractors in parity games building blocks for partial solvers"):
  Abstract: Formal methods and verification rely heavily on algorithms that compute which states of a model satisfy a specified property. The un- derlying decision problems are often undecidable or have prohibitive com- plexity. Consequently, many algorithms represent partial solvers that may not terminate or report inconclusive results on some inputs but whose termi- nating, conclusive outputs are correct. It is therefore surprising that partial solvers have not yet been studied in verification based on parity games, a problem that is known to be polynomial-time equivalent to the model check- ing for modal mu-calculus. We here provide what appears to be the first such in-depth technical study by developing the required foundations for such an approach to solving parity games and by experimentally validating the utility of these foundations. Attractors in parity games are a technical device for solving "alternat- ing" reachability of given node sets. A well known solver of parity games - Zielonka's algorithm - uses such attractor computations recursively. We here propose new forms of attractors that are monotone in that they are aware of specific static patterns of colors encountered in reaching a given node set in alternating fashion. Then we demonstrate how these new forms of attractors can be embedded within greatest fixed-point computations to design solvers of parity games that run in polynomial time but are partial in that they may not decide the winning status of all nodes in the input game. Experimental results show that our partial solvers completely solve bench- marks that were constructed to challenge existing full solvers. Our partial solvers also have encouraging run times in practice. For one partial solver we prove that its runtime is at most cubic in the number of nodes in the parity game, that its output game is independent of the order in which monotone attractors are computed, and that it solves all Buchi games and weak games. We then define and study a transformation that converts partial solvers into more precise partial solvers, and we prove that this transformation is

Includes members:
  Member "zielonka s recursive algorithm dull weak and solitaire games and tighter bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a parity game tale of two counters")
  Member "an o n 2 time algorithm for alternating b uchi games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cegar for qualitative analysis of probabilistic systems")
  Member "automatic verification of correspondences for security protocols":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "formal analysis of an e health protocol")

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)