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 "dependency weighted aggregation on factorized databases"):
  Abstract: We study a new class of aggregation problems, called dependency weighted aggregation. The underlying idea is to aggregate the answer tuples of a query while accounting for dependencies between them, where two tuples are considered dependent when they have the same value on some attribute. The main problem we are interested in is to compute the dependency weighted count of a conjunctive query. This aggregate can be seen as a form of weighted counting, where the weights of the answer tuples are computed by solving a linear program. This linear program enforces that dependent tuples are not over represented in the final weighted count. The dependency weighted count can be used to compute the s-measure, a measure that is used in data mining to estimate the frequency of a pattern in a graph database. Computing the dependency weighted count of a conjunctive query is NP-hard in general. In this paper, we show that this problem is actually tractable for a large class of structurally restricted conjunctive queries such as acyclic or bounded hypertree width queries. Our algorithm works on a factorized representation of the answer set, in order to avoid enumerating it exhaustively. Our technique produces a succinct representation of the weighting of the answers. It can be used to solve other dependency weighted aggregation tasks, such as computing the (dependency) weighted average of the value of an attribute in the answers set.

Includes members:
  Member "constraint solving via fractional edge covers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "hypergraph acyclicity and propositional model counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the complexity of sat using knowledge compilation")
  Member "gym a multiround join algorithm in mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "structural tractability of counting of solutions to conjunctive queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counting answers to existential questions")
  Member "hypertree decompositions and tractable queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "a knowledge compilation map":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "abstracting probabilistic models a logical perspective")

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)