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: "scalarization methods for many objective virtual machine placement of elastic infrastructures in overbooked cloud computing data centers under uncertainty"
  Abstract: Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. #R##N#The first part of this work analyzes alternatives to solve the formulated problem, an experimental comparison of five different online deterministic heuristics against an offline memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. #R##N#This work presents a two-phase schema formulation of a VMP problem considering the optimization of three objective functions in an IaaS environment with elasticity and overbooking capabilities. The two-phase schema formulation describes that the allocation of the VMs can be separated into two sub-problems, the incremental allocation (iVMP) and the reconfiguration of a placement (VMPr). #R##N#To analyze alternatives to solve the formulated problem, an experimental comparison of three different objective function scalarization methods as part of the iVMP and VMPr was performed considering several experimental workloads. Simulations indicate that the Euclidean distance to origin outperforms other evaluated scalarization methods on average. #R##N#In order to portray the dynamic nature of an IaaS environment a customizable workload trace generator was developed to simulate uncertainty in the scenarios with elasticity and overbooking of resources in VM requests. #R##N#Experimental results proved that the Euclidean distance is preferable over the other scalarizatiom methods to improve the values of the power consumption objective function.

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)