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 "context aware distribution of fog applications using deep reinforcement learning"):
  Abstract: Fog computing is an emerging paradigm that aims to meet the increasing computation demands arising from the billions of devices connected to the Internet. Offloading services of an application from the Cloud to the edge of the network can improve the overall Quality-of-Service (QoS) of the application since it can process data closer to user devices. Diverse Fog nodes ranging from Wi-Fi routers to mini-clouds with varying resource capabilities makes it challenging to determine which services of an application need to be offloaded. In this paper, a context-aware mechanism for distributing applications across the Cloud and the Fog is proposed. The mechanism dynamically generates (re)deployment plans for the application to maximise the performance efficiency of the application by taking the QoS and running costs into account. The mechanism relies on deep Q-networks to generate a distribution plan without prior knowledge of the available resources on the Fog node, the network condition and the application. The feasibility of the proposed context-aware distribution mechanism is demonstrated on two use-cases, namely a face detection application and a location-based mobile game. The benefits are increased utility of dynamic distribution in both use cases, when compared to a static distribution approach used in existing research.

Includes members:
  Member "feasibility of fog computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dyverse dynamic vertical scaling in multi tenant edge environments")
  Member "defog fog computing benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelling fog offloading performance")
  Member "joint communication computation caching and control in big data multi access edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "online learning for offloading and autoscaling in energy harvesting mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "enorm a framework for edge node resource management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lucid a practical lightweight deep learning solution for ddos attack detection")
  Member "fog radio access networks mobility management interference mitigation and resource optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey")
  Member "a survey and taxonomy of resource optimisation for executing bag of task applications on public clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a bag of tasks scheduler tolerant to temporal failures in clouds")
  Member "challenges and opportunities in edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "modelling fog offloading performance")

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)