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: "defog fog computing benchmarks"
  Abstract: Fog computing envisions that deploying services of an application across resources in the cloud and those located at the edge of the network may improve the overall performance of the application when compared to running the application on the cloud. However, there are currently no benchmarks that can directly compare the performance of the application across the cloud-only, edge-only and cloud-edge deployment platform to obtain any insight on performance improvement. This paper proposes DeFog, a first Fog benchmarking suite to: (i) alleviate the burden of Fog benchmarking by using a standard methodology, and (ii) facilitate the understanding of the target platform by collecting a catalogue of relevant metrics for a set of benchmarks. The current portfolio of DeFog benchmarks comprises six relevant applications conducive to using the edge. Experimental studies are carried out on multiple target platforms to demonstrate the use of DeFog for collecting metrics related to application latencies (communication and computation), for understanding the impact of stress and concurrent users on application latencies, and for understanding the performance of deploying different combination of services of an application across the cloud and edge. DeFog is available for public download (this https URL).

Connected through hyperedge HE-1 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning"):
  with members:
    - "feasibility of fog computing"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "enorm a framework for edge node resource management"
    - "fog radio access networks mobility management interference mitigation and resource optimization"
    - "a survey and taxonomy of resource optimisation for executing bag of task applications on public clouds"
    - "challenges and opportunities in edge computing"
    - "decentralized computation offloading game for mobile cloud computing"

Connected through hyperedge HE-2 (induced by source paper "modelling fog offloading performance"):
  with members:
    - "performance estimation of container based cloud to fog offloading"
    - "resource management in fog edge computing a survey"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"
    - "enorm a framework for edge node resource management"
    - "challenges and opportunities in edge computing"

Connected through hyperedge HE-3 (induced by source paper "dyverse dynamic vertical scaling in multi tenant edge environments"):
  with members:
    - "performance estimation of container based cloud to fog offloading"
    - "feasibility of fog computing"
    - "realizing edge marketplaces challenges and opportunities"
    - "a survey on mobile edge computing the communication perspective"
    - "enorm a framework for edge node resource management"
    - "joint subcarrier and cpu time allocation for mobile edge computing"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "energy efficient resource allocation for mobile edge computation offloading"

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)