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 "a cloud infrastructure service recommendation system for optimizing real time qos provisioning constraints"):
  Abstract: Proliferation of cloud computing has revolutionized hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service, Microsoft Azure) and small companies (e.g. Rackspace, Ninefold), decision makers (e.g. application developers, CIOs) are likely to be overwhelmed by choices available. The decision making problem is further complicated due to heterogeneous service configurations and application provisioning Quality of Service (QoS) constraints. To address this hard challenge, in our previous work we developed a semi-automated, extensible, and ontology-based approach to infrastructure service discovery and selection based on only design time constraints (e.g., renting cost, datacentre location, service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (endto- end message latency, end-to-end message throughput) in the decision making process. Hosting of next generation applications in domain of on-line interactive gaming, large scale sensor analytics, and real-time mobile applications on cloud services necessitates optimization of such real-time QoS constraints for meeting Service Level Agreements (SLAs). To this end, we present a real-time QoS aware multi-criteria decision making technique that builds over well known Analytics Hierarchy Process (AHP) method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute possible best fit combinations of cloud services at IaaS layer. We conducted extensive experiments to prove the feasibility of our approach.

Includes members:
  Member "cloudgenius decision support for web server cloud migration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stackinsights cognitive learning for hybrid cloud readiness")
  Member "investigating decision support techniques for automating cloud service selection":
    also connected through other hyperedges:
      (none)
  Member "an ontology based system for cloud infrastructure services discovery":
    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.DC(Distributed, Parallel, and Cluster Computing)