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: "separating a voronoi diagram"
  Abstract: Given a set $\mathsf{P}$ of $n$ points in $\mathbb{R}^d$, we show how to insert a set $\mathsf{X}$ of $O( n^{1-1/d} )$ additional points, such that $\mathsf{P}$ can be broken into two sets $\mathsf{P}_1$ and $\mathsf{P}_2$, of roughly equal size, such that in the Voronoi diagram $\mathcal{V}( \mathsf{P} \cup \mathsf{X} )$, the cells of $\mathsf{P}_1$ do not touch the cells of $\mathsf{P}_2$; that is, $\mathsf{X}$ separates $\mathsf{P}_1$ from $\mathsf{P}_2$ in the Voronoi diagram. #R##N#Given such a partition $(\mathsf{P}_1,\mathsf{P}_2)$ of $\mathsf{P}$, we present approximation algorithms to compute the minimum size separator realizing this partition.

Connected through hyperedge HE-1 (induced by source paper "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"):
  with members:
    - "the power of local search for clustering"
    - "local search yields a ptas for k means in doubling metrics"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "approximating k median via pseudo approximation"
    - "improved spectral norm bounds for clustering"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-2 (induced by source paper "the power of local search for clustering"):
  with members:
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "approximating k median via pseudo approximation"
    - "a unified framework for approximating and clustering data"
    - "parallel approximation algorithms for facility location problems"

Connected through hyperedge HE-3 (induced by source paper "on variants of k means clustering"):
  with members:
    - "a bi criteria approximation algorithm for k means"
    - "the hardness of approximation of euclidean k means"
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "simpler analyses of local search algorithms for facility location"

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.CG(Computational Geometry)