human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the benefits of qos differentiated posted pricing in cloud computing an analytical model"
  Abstract: When designing service and pricing models, an important goal is to both enable user-friendly and cost-effective access to services and achieve a high system's utilization. Such design could attract more users and improve the revenue. QoS-differentiated pricing represents an important direction to address the above design goal as illustrated by numerous studies in other fields such as Internet. In this paper, we propose the first analytical model of QoS-differentiated posted pricing in the context of cloud computing. In this model, a cloud provider offers a stable set of SLAs and their prices are also posted to users in advance, where higher QoSs are charged higher prices. As a result, users can directly choose their preferred SLA with a certain price, in contrast to many existing dynamic pricing mechanisms with the uncertain prices and availability of cloud resource. #R##N#In order to maximizing the revenue of a cloud provider, the questions that arise here include: (1) setting the prices properly to direct users to the right SLAs best fitting their need, and, (2) given a fixed cloud capacity, determining how many servers should be assigned to each SLA and which users and how many of their jobs are admitted to be served. To this respect, we propose optimal schemes to jointly determine SLA-based prices and perform capacity planning. In spite of the usability of such a pricing model, simulations also show that it could improve the utilization of computing resource by up to eleven-fold increase, compared with the standard pay-as-you-go pricing model; furthermore, the revenue of a cloud provider could be improved by up to a five-fold increase.

Connected through hyperedge HE-1 (induced by source paper "a framework for allocating server time to spot and on demand services in cloud computing"):
  with members:
    - "towards designing cost optimal policies to utilize iaas clouds under online learning"
    - "a truthful 1 epsilon optimal mechanism for on demand cloud resource provisioning"
    - "spot pricing in the cloud ecosystem a comparative investigation"
    - "truthful online scheduling with commitments"
    - "algorithms for scheduling deadline sensitive malleable tasks"
    - "fixed and market pricing for cloud services"

Question: Please tell me which class the center node 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)