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: "guarantees and limits of preprocessing in constraint satisfaction and reasoning"
  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 under structural restrictions. All these problems involve two tasks: (i) identifying the structure in the input as required by the restriction, and (ii) using the identified structure to solve the reasoning task efficiently. We show that for most of the considered problems, task (i) admits a polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, in contrast to task (ii) which does not admit such a reduction to a problem kernel of polynomial size, subject to a complexity theoretic assumption. As a notable exception we show that the consistency problem for the AtMost-NValue constraint admits a polynomial kernel consisting of a quadratic number of variables and domain values. Our results provide a firm worst-case guarantees and theoretical boundaries 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"
    - "efficient classification for metric data"
    - "parameterized complexity and kernel bounds for hard planning problems"
    - "limits of preprocessing"
    - "kernels for global constraints"

Connected through hyperedge HE-2 (induced by source paper "complexity and approximability of parameterized max csps"):
  with members:
    - "solving maxsat and sat on structured cnf formulas"
    - "tractable answer set programming with weight constraints bounded treewidth is not enough"
    - "model counting for formulas of bounded clique width"
    - "a characterization of approximation resistance for even k partite csps"
    - "satisfiability of acyclic and almost acyclic cnf formulas"
    - "majority is stablest discrete and sos"
    - "backdoors to acyclic sat"
    - "not so easy problems for tree decomposable graphs"

Connected through hyperedge HE-3 (induced by source paper "meta kernelization with structural parameters"):
  with members:
    - "meta kernelization using well structured modulators"
    - "kernelization using structural parameters on sparse graph classes"
    - "preprocessing for treewidth a combinatorial analysis through kernelization"
    - "linear time split decomposition revisited"

Connected through hyperedge HE-4 (induced by source paper "backdoors to tractable answer set programming"):
  with members:
    - "the parameterized complexity of reasoning problems beyond np"
    - "backdoors to normality for disjunctive logic programs"
    - "numvc an efficient local search algorithm for minimum vertex cover"
    - "backdoors to abduction"
    - "the good the bad and the odd cycles in answer set programs"
    - "strong backdoors to bounded treewidth sat"
    - "strong backdoors to nested satisfiability"
    - "directed subset feedback vertex set is fixed parameter tractable"

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)