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: "using pivot consistency to decompose and solve functional csps"
  Abstract: Many studies have been carried out in order to increase the search efficiency of constraint satisfaction problems; among them, some make use of structural properties of the constraint network; others take into account semantic properties of the constraints, generally assuming that all the constraints possess the given property. In this paper, we propose a new decomposition method benefiting from both semantic properties of functional constraints (not bijective constraints) and structural properties of the network; furthermore, not all the constraints need to be functional. We show that under some conditions, the existence of solutions can be guaranteed. We first characterize a particular subset of the variables, which we name a root set. We then introduce pivot consistency, a new local consistency which is a weak form of path consistency and can be achieved in O(n^2d^2) complexity (instead of O(n^3d^3) for path consistency), and we present associated properties; in particular, we show that any consistent instantiation of the root set can be linearly extended to a solution, which leads to the presentation of the aforementioned new method for solving by decomposing functional CSPs.

Connected through hyperedge HE-1 (induced by source paper "a join based hybrid parameter for constraint satisfaction"):
  with members:
    - "binary constraint satisfaction problems defined by excluded topological minors"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-2 (induced by source paper "hybrid tractability of valued constraint problems"):
  with members:
    - "the complexity of conservative valued csps"
    - "map estimation message passing and perfect graphs"
    - "generalising tractable vcsps defined by symmetric tournament pair multimorphisms"

Connected through hyperedge HE-3 (induced by source paper "second order consistencies"):
  with members:
    - "set intersection and consistency in constraint networks"
    - "domain filtering consistencies"
    - "the essence of constraint propagation"

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)