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 "performance considerations on execution of large scale workflow applications on cloud functions"):
  Abstract: Function-as-a-Service is a novel type of cloud service used for creating distributed applications and utilizing computing resources. Application developer supplies source code of cloud functions, which are small applications or application components, while the service provider is responsible for provisioning the infrastructure, scaling and exposing a REST style API. This environment seems to be adequate for running scientific workflows, which in recent years, have become an established paradigm for implementing and preserving complex scientific processes. In this paper, we present work done on evaluating three major FaaS providers (Amazon, Google, IBM) as a platform for running scientific workflows. The experiments were performed with a dedicated benchmarking framework, which consisted of instrumented workflow execution engine. The testing load was implemented as a large scale bag-of-tasks style workflow, where task count reached 5120 running in parallel. The studied parameters include raw performance, efficiency of infrastructure provisioning, overhead introduced by the API and network layers, as well as aspects related to run time accounting. Conclusions include insights into available performance, expressed as raw GFlops values and charts depicting relation of performance to function size. The infrastructure provisioning proved to be governed by parallelism and rate limits, which can be deducted from included charts. The overhead imposed by using a REST API proved to be a significant contribution to overall run time of individual tasks, and possibly the whole workflow. The paper ends with pointing out possible future work, which includes building performance models and designing a dedicated scheduling algorithms for running scientific workflows on FaaS.

Includes members:
  Member "occupy the cloud distributed computing for the 99":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serverless straggler mitigation using local error correcting codes")
  Member "serverless computing current trends and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serverless straggler mitigation using local error correcting codes")

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)