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 "serving online demands with movable centers"):
  Abstract: We study an online problem in which a set of mobile resources or centers have to be moved in order to optimally serve a set of demands. There is a set of $n$ nodes and a set of $k$ resources that are placed at some of the nodes. Each node can potentially host several resources and the resources can be moved between the nodes. At the nodes, demands arrive in an online fashion, and the cost for serving the demands is a function of the number of demands and resources at the different nodes. An online algorithm has to move resources in order to keep the service cost low, however as moving resources is expensive, the objective of an online algorithm is to minimize the total number of movements. #R##N#Specifically, we give a deterministic online algorithm, which for parameters $\alpha\geq 1$ and $\beta\geq 0$ guarantees that at all times, the service cost is within a multiplicative factor $\alpha$ and an additive term $\beta$ of the optimal service cost and where also the movement cost is within a small multiplicative factor and an additive term of the optimal overall cost. Roughly, for $\alpha=1$ and $\beta=\Omega(k)$, we show that the movement cost by time $t$ is upper bounded by the optimal service cost at time $t$ plus an additive term of order $O(k\log k)$. For $\alpha>1$ and arbitrary $\beta$, we show that the movement cost by time $t$ is only linear in $k\log k$ and logarithmic in the optimal service cost at time $t$. We also show that both bounds are almost asymptotically tight.

Includes members:
  Member "local search based approximation algorithms for mobile facility location problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sets clustering")
  Member "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constant factor approximation algorithms for parity constrained facility location problems")
  Member "greedy facility location algorithms analyzed using dual fitting with factor revealing lp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cache optimization models and algorithms")

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.DS(Data Structures and Algorithms)