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 "container based cloud virtual machine benchmarking"):
  Abstract: With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight - time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed. In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near real-time on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite - Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.

Includes members:
  Member "analysis of docker security":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "agile network access control in the container age")
  Member "an introduction to docker for reproducible research with examples from the r environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "openedgar open source software for sec edgar analysis")
  Member "optimal deployment of geographically distributed workflow engines on the cloud":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning")
  Member "cloud benchmarking for performance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferable knowledge for low cost decision making in cloud environments")
  Member "location location location data intensive distributed computing in the cloud":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "executing bag of distributed tasks on virtually unlimited cloud resources")
  Member "parallel simulations for analysing portfolios of catastrophic event risk":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cloud benchmarking for maximising performance of scientific applications")
  Member "on a catalogue of metrics for evaluating commercial cloud services":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "defog fog computing benchmarks")

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)