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 "effects of component subscription network topology on large scale data centre performance scaling"):
  Abstract: Modern large-scale date centres, such as those used for cloud computing service provision, are becoming ever-larger as the operators of those data centres seek to maximise the benefits from economies of scale. With these increases in size comes a growth in system complexity, which is usually problematic. There is an increased desire for automated "self-star" configuration, management, and failure-recovery of the data-centre infrastructure, but many traditional techniques scale much worse than linearly as the number of nodes to be managed increases. As the number of nodes in a median-sized data-centre looks set to increase by two or three orders of magnitude in coming decades, it seems reasonable to attempt to explore and understand the scaling properties of the data-centre middleware before such data-centres are constructed. In [1] we presented SPECI, a simulator that predicts aspects of large-scale data-centre middleware performance, concentrating on the influence of status changes such as policy updates or routine node failures. The initial version of SPECI was based on the assumption (taken from our industrial sponsor, a major data-centre provider) that within the data-centre there will be components that work together and need to know the status of other components via "subscriptions" to status-updates from those components. In [1] we used a first-approximation assumption that such subscriptions are distributed wholly at random across the data centre. In this present paper, we explore the effects of introducing more realistic constraints to the structure of the internal network of subscriptions. We contrast the original results from SPECI with new results from simulations exploring the effects of making the data-centre's subscription network have a regular lattice-like structure, and also semi-random network structures resulting from parameterised network generation functions that create "small-world" and "scale-free" networks. We show that for distributed middleware topologies, the structure and distribution of tasks carried out in the data centre can significantly influence the performance overhead imposed by the middleware.

Includes members:
  Member "speci a simulation tool exploring cloud scale data centres":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "usage of cloud computing simulators and future systems for computational research")
  Member "modeling and simulation of scalable cloud computing environments and the cloudsim toolkit challenges and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "iotnetsim a modelling and simulation platform for end to end iot services and networking")
  Member "decentralized overlay for federation of enterprise clouds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aneka a software platform for net based cloud computing")

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)