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: "about the suitability of clouds in high performance computing"
  Abstract: Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM, which were used as examples. 1. I NTRODUCTION Cloud computing has become the new ubiquitous computing and storage paradigm. Reasons are e.g. the pay-as-you-go accounting, the inherent elasticity, flexibility and user customizing. An example therefore is the arbitrary creation and deletion of virtual machines (VMs) with individual numbers of vCPUs and flexible amounts of virtual main memory per VM. Companies, institutions and individuals can profit from this paradigm by off-loading computing and storage to commercial cloud service providers (CSPs), because CSP services range from simple data backups to entire virtual data centres. These advantages make cloud computing also attractive for scientists, since they do not need any more to provide and maintain their own computer infrastructure, but can out-source it to CSPs, which is then hosted as virtual IT. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or Big Data analysis, things are getting more intricate, because HPC codes must stay highly efficient and thus scalable, even when executed by many virtual cores (vCPUs), which are located on different physical servers. This is not necessarily the case in older clouds and also not in newer standard clouds, as measurements made by several research groups have shown. For example, the US Dep. of Energy (DoE), which is responsible for HPC in the USA, has questioned the usefulness of clouds for HPC in 2011 [1] in general. Other authors with the same opinion are [2]-[4], for example. We believe that further research effort is needed to improve the execution efficiency and the speed-up for HPC in clouds, but it is also our opinion that this is possible. In this contribution, multiple reasons for cloud inefficiencies are presented for the case of OpenStack [5] as cloud operating system and for OpenFOAM [6] as HPC example code. Suggestions are made how to solve or circumvent them. The results can be extrapolated to other cloud OSes and other HPC codes. The contribution is organized as follows: In chapter 2, the state-of-the-art is reviewed. Chapter 3 describes our project and what equipment and tools we were using. In

Connected through hyperedge HE-1 (induced by source paper "hpc cloud for scientific and business applications taxonomy vision and research challenges"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "sla aware interactive workflow assistant for hpc parameter sweeping experiments"
    - "job placement advisor based on turnaround predictions for hpc hybrid clouds"
    - "tensorflow a system for large scale machine learning"
    - "multi cloud resource provisioning with aneka a unified and integrated utilisation of microsoft azure and amazon ec2 instances"
    - "big data computing and clouds"
    - "high performance cloud computing a view of scientific applications"
    - "the anatomy of the grid enabling scalable virtual organizations"

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)