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: "parity games imperfect information and structural complexity"
  Abstract: We address the problem of solving parity games with imperfect information on finite graphs of bounded structural complexity. It is a major open problem whether parity games with perfect information can be solved in PTIME. Restricting the structural complexity of the game arenas, however, often leads to efficient algorithms for parity games. Such results are known for graph classes of bounded tree-width, DAG-width, directed path-width, and entanglement, which we describe in terms of cops and robber games. Conversely, the introduction of imperfect information makes the problem more difficult, it becomes EXPTIME-hard. We analyse the interaction of both approaches. #R##N#We use a simple method to measure the amount of "unawareness"' of a player, the amount of imperfect information. It turns out that if it is unbounded, low structural complexity does not make the problem simpler. It remains EXPTIME-hard or PSPACE-hard even on very simple graphs. #R##N#For games with bounded imperfect information we analyse the powerset construction, which is commonly used to convert a game of imperfect information into an equivalent game with perfect information. This construction preserves boundedness of directed path-width and DAG-width, but not of entanglement or of tree-width. Hence, if directed path-width or DAG-width are bounded, parity games with bounded imperfect information can be solved in PTIME. For DAG-width we follow two approaches. One leads to a generalization of the known fact that perfect information parity games are in PTIME if DAG-width is bounded. We prove this theorem for non-monotone DAG-width. The other approach introduces a cops and robbers game (with multiple robbers) on directed graphs, considered Richerby and Thilikos forundirected graphs. We show a tight linear bound for the number of additional cops needed to capture an additional robber.

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.GT(Computer Science and Game Theory)