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: "the unreasonable success of local search geometric optimization"
  Abstract: What is the effectiveness of local search algorithms for geometric problems in the plane? We prove that local search with neighborhoods of magnitude $1/\epsilon^c$ is an approximation scheme for the following problems in the Euclidian plane: TSP with random inputs, Steiner tree with random inputs, facility location (with worst case inputs), and bicriteria $k$-median (also with worst case inputs). The randomness assumption is necessary for TSP.

Connected through hyperedge HE-1 (induced by source paper "stability and recovery for independence systems"):
  with members:
    - "k center clustering under perturbation resilience"
    - "computational feasibility of clustering under clusterability assumptions"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "improved approximation for 3 dimensional matching via bounded pathwidth local search"
    - "large neighborhood local search for the maximum set packing problem"
    - "clustering is difficult only when it does not matter"
    - "on the practically interesting instances of maxcut"
    - "nash equilibria in perturbation resilient games"

Connected through hyperedge HE-2 (induced by source paper "local search yields a ptas for k means in doubling metrics"):
  with members:
    - "the power of local search for clustering"
    - "theoretical analysis of the k means algorithm a survey"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-3 (induced by source paper "simple ptas s for families of graphs excluding a minor"):
  with members:
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "approximation algorithms for dominating set in disk graphs"
    - "single source shortest paths in h minor free graphs"

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)