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: "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.

Connected through hyperedge HE-1 (induced by source paper "defog fog computing benchmarks"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "yolov3 an incremental improvement"
    - "edgebench benchmarking edge computing platforms"
    - "enorm a framework for edge node resource management"
    - "doclite a docker based lightweight cloud benchmarking tool"
    - "challenges and opportunities in edge computing"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "cloud brokerage a systematic survey"):
  with members:
    - "price and performance of cloud hosted virtual network functions analysis and future challenges"
    - "same same but different a descriptive differentiation of intra cloud iaas services"
    - "daleel simplifying cloud instance selection using machine learning"
    - "socloud a service oriented component based paas for managing portability provisioning elasticity and high availability across multiple clouds"
    - "cloud services brokerage a survey and research roadmap"
    - "academic cloud computing research five pitfalls and five opportunities"
    - "experiences of using a hybrid cloud to construct an environmental virtual observatory"
    - "vm mad a cloud cluster software for service oriented academic environments"

Connected through hyperedge HE-3 (induced by source paper "dockerization impacts in database performance benchmarking"):
  with members:
    - "plug and play bench simplifying big data benchmarking using containers"
    - "characterizing and adapting the consistency latency tradeoff in distributed key value stores"

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)