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 "exploiting opportunistic physical design in large scale data analytics"):
  Abstract: Large-scale systems, such as MapReduce and Hadoop, perform aggressive materialization of intermediate job results in order to support fault tolerance. When jobs correspond to exploratory queries submitted by data analysts, these materializations yield a large set of materialized views that typically capture common computation among successive queries from the same analyst, or even across queries of different analysts who test similar hypotheses. We propose to treat these views as an opportunistic physical design and use them for the purpose of query optimization. We develop a novel query-rewrite algorithm that addresses the two main challenges in this context: how to search the large space of rewrites, and how to reason about views that contain UDFs (a common feature in large-scale data analytics). The algorithm, which provably finds the minimum-cost rewrite, is inspired by nearest-neighbor searches in non-metric spaces. We present an extensive experimental study on real-world datasets with a prototype data-analytics system based on Hive. The results demonstrate that our approach can result in dramatic performance improvements on complex data-analysis queries, reducing total execution time by an average of 61% and up to two orders of magnitude.

Includes members:
  Member "towards a workload for evolutionary analytics":
    also connected through other hyperedges:
      (none)
  Member "restore reusing results of mapreduce jobs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a taxonomy on big data survey")
  Member "opening the black boxes in data flow optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the nebulastream platform data and application management for the internet of things")

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)