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 approximation schemes for uniform cost clustering problems in planar graphs"
  Abstract: We consider the $k$-Median problem on planar graphs: given an edge-weighted planar graph $G$, a set of clients $C \subseteq V(G)$, a set of facilities $F \subseteq V(G)$, and an integer parameter $k$, the task is to find a set of at most $k$ facilities whose opening minimizes the total connection cost of clients, where each client contributes to the cost with the distance to the closest open facility. We give two new approximation schemes for this problem: -- FPT Approximation Scheme: for any $\epsilon>0$, in time $2^{O(k\epsilon^{-3}\log (k\epsilon^{-1}))}\cdot n^{O(1)}$ we can compute a solution that #R##N#(1) has connection cost at most $(1+\epsilon)$ times the optimum, with high probability. -- Efficient Bicriteria Approximation Scheme: for any $\epsilon>0$, in time $2^{O(\epsilon^{-5}\log (\epsilon^{-1}))}\cdot n^{O(1)}$ we can compute a set of at most $(1+\epsilon)k$ facilities #R##N#(2) whose opening yields connection cost at most $(1+\epsilon)$ times the optimum connection cost for opening at most $k$ facilities, with high probability. #R##N#As a direct corollary of the second result we obtain an EPTAS for the Uniform Facility Location on planar graphs, with same running time. #R##N#Our main technical tool is a new construction of a "coreset for facilities" for $k$-Median in planar graphs: we show that in polynomial time one can compute a subset of facilities $F_0\subseteq F$ of size $k\cdot (\log n/\epsilon)^{O(\epsilon^{-3})}$ with a guarantee that there is a $(1+\epsilon)$-approximate solution contained in $F_0$.

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)