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 "partitioning indexing and querying spatial data on cloud"):
  Abstract: The number of mobile devices (e.g., smartphones, wearable technologies) is rapidly growing. In line with this trend, a massive amount of spatial data is being collected since these devices allow users to geo-tag user-generated content. Clearly, a scalable computing infrastructure is needed to manage such large datasets. Meanwhile, Cloud Computing service providers (e.g., Amazon, Google, and Microsoft) allow users to lease computing resources. However, most of the existing spatial indexing techniques are designed for the centralized paradigm which is limited to the capabilities of a single sever. To address the scalability shortcomings of existing approaches, we provide a study that focus on generating a distributed spatial index structure that not only scales out to multiple servers but also scales up since it fully exploits the multi-core CPUs available on each server using Voronoi diagram as the partitioning and indexing technique which we also use to process spatial queries effectively. More specifically, since the data objects continuously move and issue position updates to the index structure, we collect the latest positions of objects and periodically generate a read-only index to eliminate costly distributed updates. Our approach scales near-linearly in index construction and query processing, and can efficiently construct an index for millions of objects within a few seconds. In addition to scalability and efficiency, we also aim to maximize the server utilization that can support the same workload with less number of servers. Server utilization is a crucial point while using Cloud Computing because users are charged based on the total amount of time they reserve each server, with no consideration of utilization.

Includes members:
  Member "efficient processing of k nearest neighbor joins using mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on geographically distributed big data processing using mapreduce")
  Member "boosting moving object indexing through velocity partitioning":
    also connected through other hyperedges:
      (none)

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)