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 "clustering in a 2 dimensional pareto front the p median and p center problems are polynomially solvable"):
  Abstract: This paper is motivated by a real life application of multi-objective optimization without preference. Having many non-dominated solutions with Pareto dominance, the problem is to select and present a small number of representative solutions to decision makers. The p-median and p-center clustering problems are investigated in this paper for the 2-dimensional case assuming that the points to cluster are not comparable in $\mathbb{R}^2$. This paper proves that these clustering problems can be solved to optimality with a unified dynamic programming algorithm. Having $N$ points to partition in $K$ clusters, a polynomial complexity is proven in $O(N^3)$ for the p-median problem, in $O(N^2.(K + \log N))$ for the discrete p-center problem and in $O(K.N^2)$ for the continuous p-center problem. The dynamic programming algorithm can be adapted to consider cardinality constraints for the clusters, which can improve the complexities. These algorithms allows furthermore a natural parallel implementation. A posteriori, the complexity of the clustering algorithms allows also to consider these algorithms inside multi-objective meta-heuristics to archive diversified non-dominated points along the Pareto front.

Includes members:
  Member "maximum volume subset selection for anchored boxes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polynomial algorithms for p dispersion problems in a 2d pareto front")
  Member "fast exact k means k medians and bregman divergence clustering in 1d":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polynomial algorithms for p dispersion problems in a 2d pareto front")
  Member "optimal interval clustering application to bregman clustering and statistical mixture learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")

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.CC(Computational Complexity)