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 "addressing the challenges in federating edge resources"):
  Abstract: This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.

Includes members:
  Member "online learning for offloading and autoscaling in energy harvesting mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning")
  Member "edge as a service towards distributed cloud architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource management in fog edge computing a survey")
  Member "challenges and opportunities in edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning")
  Member "dynamic service migration in mobile edge clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "software defined networking state of the art and research challenges":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sdn based qos provision in wsn technologies")

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)