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: "efficient online strategies for renting servers in the cloud"
  Abstract: In Cloud systems, we often deal with jobs that arrive and depart in an online manner. Upon its arrival, a job should be assigned to a server. Each job has a size which defines the amount of resources that it needs. Servers have uniform capacity and, at all times, the total size of jobs assigned to a server should not exceed the capacity. This setting is closely related to the classic bin packing problem. The difference is that, in bin packing, the objective is to minimize the total number of used servers. In the Cloud, however, the charge for each server is proportional to the length of the time interval it is rented for, and the goal is to minimize the cost involved in renting all used servers. Recently, certain bin packing strategies were considered for renting servers in the Cloud [Li et al. SPAA'14]. There, it is proved that all Any-Fit bin packing strategy has a competitive ratio of at least $\mu$, where $\mu$ is the max/min interval length ratio of jobs. It is also shown that First Fit has a competitive ratio of $2\mu + 13$ while Best Fit is not competitive at all. We observe that the lower bound of $\mu$ extends to all online algorithms. We also prove that, surprisingly, Next Fit algorithm has competitive ratio of at most $2 \mu +1$. We also show that a variant of Next Fit achieves a competitive ratio of $K \times max\{1,\mu/(K-1)\}+1$, where $K$ is a parameter of the algorithm. In particular, if the value of $\mu$ is known, the algorithm has a competitive ratio of $\mu+2$; this improves upon the existing upper bound of $\mu+8$. Finally, we introduce a simple algorithm called Move To Front (MTF) which has a competitive ratio of at most $6\mu + 7$ and also promising average-case performance. We experimentally study the average-case performance of different algorithms and observe that the typical behaviour of MTF is distinctively better than other algorithms.

Connected through hyperedge HE-1 (induced by source paper "reducing the upfront cost of private clouds with clairvoyant virtual machine placement"):
  with members:
    - "a genetic algorithm for power aware virtual machine allocation in private cloud"

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.DS(Data Structures and Algorithms)