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: "a polynomial time approximation scheme for facility location on planar graphs"
  Abstract: We consider the classic Facility Location problem on planar graphs (non-uniform, uncapacitated). Given an edge-weighted planar graph $G$, a set of clients $C\subseteq V(G)$, a set of facilities $F\subseteq V(G)$, and opening costs $\mathsf{open} \colon F \to \mathbb{R}_{\geq 0}$, the goal is to find a subset $D$ of $F$ that minimizes $\sum_{c \in C} \min_{f \in D} \mathrm{dist}(c,f) + \sum_{f \in D} \mathsf{open}(f)$. #R##N#The Facility Location problem remains one of the most classic and fundamental optimization problem for which it is not known whether it admits a polynomial-time approximation scheme (PTAS) on planar graphs despite significant effort for obtaining one. We solve this open problem by giving an algorithm that for any $\varepsilon>0$, computes a solution of cost at most $(1+\varepsilon)$ times the optimum in time $n^{2^{O(\varepsilon^{-2} \log (1/\varepsilon))}}$.

Connected through hyperedge HE-1 (induced by source paper "efficient approximation schemes for uniform cost clustering problems in planar graphs"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "quasi polynomial time approximation schemes for packing and covering problems in planar graphs"
    - "a fast approximation scheme for low dimensional k means"
    - "new frameworks for offline and streaming coreset constructions"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "quasi polynomial time approximation scheme for sparse subsets of polygons"

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)