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: "limits of preprocessing"
  Abstract: We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, such as induced width or backdoor size. Our results provide a firm theoretical boundary for the performance of polynomial-time preprocessing algorithms for the considered problems.

Connected through hyperedge HE-1 (induced by source paper "diversity in combinatorial optimization"):
  with members:
    - "what is known about vertex cover kernelization"
    - "propagation via kernelization the vertex cover constraint"
    - "guarantees and limits of preprocessing in constraint satisfaction and reasoning"
    - "efficient classification for metric data"
    - "parameterized complexity and kernel bounds for hard planning problems"
    - "kernels for global constraints"

Connected through hyperedge HE-2 (induced by source paper "guarantees and limits of preprocessing in constraint satisfaction and reasoning"):
  with members:
    - "on feedback vertex set new measure and new structures"
    - "parameterized complexity and kernel bounds for hard planning problems"
    - "backdoors to satisfaction"
    - "backdoors to tractable answer set programming"
    - "kernels for global constraints"
    - "range and roots two common patterns for specifying and propagating counting and occurrence constraints"
    - "the parameterized complexity of global constraints"
    - "cutset sampling for bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "backdoors to satisfaction"):
  with members:
    - "the good the bad and the odd cycles in answer set programs"
    - "strong backdoors to bounded treewidth sat"
    - "strong backdoors to nested satisfiability"
    - "augmenting tractable fragments of abstract argumentation"
    - "backdoors to acyclic sat"
    - "3 sat faster and simpler unique sat bounds for ppsz hold in general"
    - "backdoors to tractable answer set programming"
    - "subset feedback vertex set is fixed parameter tractable"

Connected through hyperedge HE-4 (induced by source paper "parameterized complexity results for general factors in bipartite graphs with an application to constraint programming"):
  with members:
    - "kernels for global constraints"
    - "the parameterized complexity of global constraints"

Connected through hyperedge HE-5 (induced by source paper "kernels for global constraints"):
  with members:
    - "the parameterized complexity of global constraints"

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.AI(Artificial Intelligence)