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: "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

Connected through hyperedge HE-1 (induced by source paper "partial solvers for generalized parity games"):
  with members:
    - "the 5th reactive synthesis competition syntcomp 2018 benchmarks participants results"
    - "oink an implementation and evaluation of modern parity game solvers"
    - "partial solvers for parity games effective polynomial time composition"
    - "conditionally optimal algorithms for generalized buchi games"

Connected through hyperedge HE-2 (induced by source paper "winning cores in parity games"):
  with members:
    - "benchmarks for parity games"
    - "graph operations on parity games and polynomial time algorithms"
    - "linear time algorithm for weak parity games"

Connected through hyperedge HE-3 (induced by source paper "partial solvers for parity games effective polynomial time composition"):
  with members:
    - "benchmarks for parity games"
    - "time and parallelizability results for parity games with bounded tree and dag width"
    - "the rabin index of parity games"

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)