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: "aggregation in probabilistic databases via knowledge compilation"
  Abstract: This paper presents a query evaluation technique for positive relational algebra queries with aggregates on a representation system for probabilistic data based on the algebraic structures of semiring and semimodule. The core of our evaluation technique is a procedure that compiles semimodule and semiring expressions into so-called decomposition trees, for which the computation of the probability distribution can be done in time linear in the product of the sizes of the probability distributions represented by its nodes. We give syntactic characterisations of tractable queries with aggregates by exploiting the connection between query tractability and polynomial-time decomposition trees. A prototype of the technique is incorporated in the probabilistic database engine SPROUT. We report on performance experiments with custom datasets and TPC-H data.

Connected through hyperedge HE-1 (induced by source paper "solving linear programs on factorized databases"):
  with members:
    - "ranked enumeration of conjunctive query results"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "combining relational algebra sql constraint modelling and local search"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-2 (induced by source paper "infinite probabilistic databases"):
  with members:
    - "on constrained open world probabilistic databases"
    - "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"
    - "probabilistic databases with an infinite open world assumption"
    - "markov logic in infinite domains"
    - "conditioning probabilistic databases"

Connected through hyperedge HE-3 (induced by source paper "query by provenance"):
  with members:
    - "a core calculus for provenance"

Connected through hyperedge HE-4 (induced by source paper "enframe a platform for processing probabilistic data"):
  with members:
    - "aggregation and ordering in factorised databases"
    - "uncertain centroid based partitional clustering of uncertain data"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "rex recursive delta based data centric computation"
    - "provenance for aggregate queries"
    - "conditioning probabilistic databases"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (induced by source paper "making massive probabilistic databases practical"):
  with members:
    - "provenance for aggregate queries"
    - "scalable probabilistic databases with factor graphs and mcmc"
    - "fast and simple relational processing of uncertain data"

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.DB(Databases)