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 "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.

Includes members:
  Member "backdoors to tractable answer set programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structural decompositions of epistemic logic programs")
  Member "kernels for global constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diversity in combinatorial optimization")
  Member "range and roots two common patterns for specifying and propagating counting and occurrence constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conjunctions of among constraints")
  Member "the parameterized complexity of global constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterized algorithms for the matrix completion problem")
  Member "kernel s for problems with no kernel on out trees with many leaves":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a polynomial turing kernel for weighted independent set in bull free graphs")
  Member "cutset sampling for bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks")
  Member "stable models and an alternative logic programming paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the seventh answer set programming competition design and results")

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.AI(Artificial Intelligence)