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: "an approach to incremental and modular context sensitive analysis of logic programs"
  Abstract: Context-sensitive global analysis of large code bases can be expensive, which can be specially problematic in interactive uses of analyzers. However, in practice each development iteration implies small modifications which are often isolated within a few modules, and analysis cost can be reduced by reusing the results of previous analyses. This has been achieved to date on the one hand through modular analysis, which reduce memory consumption and on the other hand often localize the computation during reanalysis mainly to the modules affected by changes. In parallel, context-sensitive incremental fixpoints have been proposed that achieve cost reductions at finer levels of granularity, such as changes in program lines. However, these fine-grained techniques are not directly applicable to modular programs. This work describes, implements, and evaluates a context-sensitive fixpoint analysis algorithm for (Constraint) Logic Programs aimed at achieving both inter-modular (coarse-grain) and intra-modular(fine-grain) incrementality, solving the problems related to propagation of the fine-grain change information and effects across module boundaries, for additions and deletions in multiple modules. #R##N#The implementation and evaluation of our algorithm shows encouraging results: the expected advantages of fine-grain incremental analysis carry over to the modular analysis context. Furthermore, the fine-grained propagation of analysis information of our algorithm improves performance with respect to traditional modular analysis even when analyzing from scratch.

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.PL(Programming Languages)