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 "minimizing total busy time for energy aware virtual machine allocation problems"):
  Abstract: This paper investigates the energy-aware virtual machine (VM) allocation problems in clouds along characteristics: multiple resources, fixed interval time and non-preemption of virtual machines. Many previous works have been proposed to use a minimum number of physical machines, however, this is not necessarily a good solution to minimize total energy consumption in the VM placement with multiple resources, fixed interval time and non-preemption. We observed that minimizing the sum of total busy time of all physical machines implies minimizing total energy consumption of physical machines. In addition to, if mapping of a VM onto physical machines have the same total busy time then the best mapping has physical machine's remaining available resource minimizing. Based on these observations, we proposed heuristic-based EM algorithm to solve the energy-aware VM allocation with fixed starting time and duration time. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or latest finishing time, or the longest duration time first, etc.) to allocate VM. We evaluate the EM using CloudSim toolkit and jobs log-traces in the Feitelson's Parallel Workloads Archive. Simulation's results show that all of EM-ST, EM-LFT and EM-LDTF algorithms could reduce total energy consumption compared to state-of-the-art of power-aware VM allocation algorithms. (e.g. Power-Aware Best-Fit Decreasing (PABFD) [7])).

Includes members:
  Member "a genetic algorithm for power aware virtual machine allocation in private cloud":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reducing the upfront cost of private clouds with clairvoyant virtual machine placement")
  Member "a taxonomy and survey of energy efficient data centers and cloud computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning based autoscaling of workflows in the cloud a survey")

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)