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 "making massive probabilistic databases practical"):
  Abstract: Existence of incomplete and imprecise data has moved the database paradigm from deterministic to proba- babilistic information. Probabilistic databases contain tuples that may or may not exist with some probability. As a result, the number of possible deterministic database instances that can be observed from a probabilistic database grows exponentially with the number of probabilistic tuples. In this paper, we consider the problem of answering both aggregate and non-aggregate queries on massive probabilistic databases. We adopt the tuple independence model, in which each tuple is assigned a probability value. We develop a method that exploits Probability Generating Functions (PGF) to answer such queries efficiently. Our method maintains a polynomial for each tuple. It incrementally builds a master polynomial that expresses the distribution of the possible result values precisely. We also develop an approximation method that finds the distribution of the result value with negligible errors. Our experiments suggest that our methods are orders of magnitude faster than the most recent systems that answer such queries, including MayBMS and SPROUT. In our experiments, we were able to scale up to several terabytes of data on TPC- H queries, while existing methods could only run for a few gigabytes of data on the same queries.

Includes members:
  Member "aggregation in probabilistic databases via knowledge compilation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving linear programs on factorized databases")
  Member "provenance for aggregate queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "priu a provenance based approach for incrementally updating regression models")
  Member "scalable probabilistic databases with factor graphs and mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")
  Member "fast and simple relational processing of uncertain data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty annotated databases a lightweight approach for approximating certain answers extended version")

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.DB(Databases)