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 "probabilistic databases with markoviews"):
  Abstract: Most of the work on query evaluation in probabilistic databases has focused on the simple tuple-independent data model, where tuples are independent random events. Several efficient query evaluation techniques exists in this setting, such as safe plans, algorithms based on OBDDs, tree-decomposition and a variety of approximation algorithms. However, complex data analytics tasks often require complex correlations, and query evaluation then is significantly more expensive, or more restrictive. In this paper, we propose MVDB as a framework both for representing complex correlations and for efficient query evaluation. An MVDB specifies correlations by views, called MarkoViews, on the probabilistic relations and declaring the weights of the view's outputs. An MVDB is a (very large) Markov Logic Network. We make two sets of contributions. First, we show that query evaluation on an MVDB is equivalent to evaluating a Union of Conjunctive Query(UCQ) over a tuple-independent database. The translation is exact (thus allowing the techniques developed for tuple independent databases to be carried over to MVDB), yet it is novel and quite non-obvious (some resulting probabilities may be negative!). This translation in itself though may not lead to much gain since the translated query gets complicated as we try to capture more correlations. Our second contribution is to propose a new query evaluation strategy that exploits offline compilation to speed up online query evaluation. Here we utilize and extend our prior work on compilation of UCQ. We validate experimentally our techniques on a large probabilistic database with MarkoViews inferred from the DBLP data.

Includes members:
  Member "dissociation and propagation for efficient query evaluation over probabilistic databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate lifted inference with probabilistic databases")
  Member "probabilistic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "tuffy scaling up statistical inference in markov logic networks using an rdbms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "scalable probabilistic databases with factor graphs and mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")
  Member "conditioning probabilistic databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "infinite probabilistic databases")
  Member "fast and simple relational processing of uncertain data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "uncertainty annotated databases a lightweight approach for approximating certain answers extended version")
  Member "a knowledge compilation map":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "on the tractable counting of theory models and its application to belief revision and truth maintenance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stochastic constraint optimization using propagation on ordered binary decision diagrams")

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)