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 "k dominant skyline join queries extending the join paradigm to k dominant skylines"):
  Abstract: Skyline queries enable multi-criteria optimization by filtering objects that are worse in all the attributes of interest than another object. To handle the large answer set of skyline queries in high-dimensional datasets, the concept of k-dominance was proposed where an object is said to dominate another object if it is better (or equal) in at least k attributes. This relaxes the full domination criterion of normal skyline queries and, therefore, produces lesser number of skyline objects. This is called the k-dominant skyline set. Many practical applications, however, require that the preferences are applied on a joined relation. Common examples include flights having one or multiple stops, a combination of product price and shipping costs, etc. In this paper, we extend the k-dominant skyline queries to the join paradigm by enabling such queries to be asked on joined relations. We call such queries KSJQ (k-dominant skyline join queries). The number of skyline attributes, k, that an object must dominate is from the combined set of skyline attributes of the joined relation. We show how pre-processing the base relations helps in reducing the time of answering such queries over the naive method of joining the relations first and then running the k-dominant skyline computation. We also extend the query to handle cases where the skyline preference is on aggregated values in the joined relation (such as total cost of the multiple legs of the flight) which are available only after the join is performed. In addition to these problems, we devise efficient algorithms to choose the value of k based on the desired cardinality of the final skyline set. Experiments on both real and synthetic datasets demonstrate the efficiency, scalability and practicality of our algorithms.

Includes members:
  Member "aggregate skyline join queries skylines with aggregate operations over multiple relations":
    also connected through other hyperedges:
      (none)
  Member "threshold phenomena in k dominant skylines of random samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from proximity to utility a voronoi partition of pareto optima")

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)