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 "toward a distributed knowledge discovery system for grid systems"):
  Abstract: During the last decade or so, we have had a deluge of data from not only science fields but also industry and commerce fields. Although the amount of data available to us is constantly increasing, our ability to process it becomes more and more difficult. Efficient discovery of useful knowledge from these datasets is therefore becoming a challenge and a massive economic need. This led to the need of developing large-scale data mining (DM) techniques to deal with these huge datasets either from science or economic applications. In this chapter, we present a new DDM system combining dataset-driven and architecture-driven strategies. Data-driven strategies will consider the size and heterogeneity of the data, while architecture driven will focus on the distribution of the datasets. This system is based on a Grid middleware tools that integrate appropriate large data manipulation operations. Therefore, this allows more dynamicity and autonomicity during the mining, integrating and processing phases

Includes members:
  Member "scalable density based distributed clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "theoretically efficient and practical parallel dbscan")
  Member "entity based peer to peer in a data grid environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "admire framework distributed data mining on data grid platforms")
  Member "the anatomy of the grid enabling scalable virtual organizations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "serverless supercomputing high performance function as a service for science")

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.DC(Distributed, Parallel, and Cluster Computing)