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 power of local search for clustering"
  Abstract: We give the first polynomial-time approximation schemes (PTASs) for the following problems: (1) uniform facility location in edge-weighted planar graphs; (2) $k$-median and $k$-means in edge-weighted planar graphs; (3) $k$-means in Euclidean spaces of bounded dimension. Our first and second results extend to minor-closed families of graphs. All our results extend to cost functions that are the $p$-th power of the shortest-path distance. The algorithm is local search where the local neighborhood of a solution $S$ consists of all solutions obtained from $S$ by removing and adding $1/\epsilon^{O(1)}$ centers.

Connected through hyperedge HE-1 (induced by source paper "an o log 1 5 n log log n approximation algorithm for mean isoperimetry and robust k means"):
  with members:
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-2 (induced by source paper "one size fits all effectiveness of local search on structured data"):
  with members:
    - "tight analysis of a multiple swap heuristic for budgeted red blue median"
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "k center clustering under perturbation resilience"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-3 (induced by source paper "constant approximation for k median and k means with outliers via iterative rounding"):
  with members:
    - "approximation schemes for clustering with outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "improved and simplified inapproximability for k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "improved approximation algorithms for matroid and knapsack median problems and applications"
    - "approximating k median via pseudo approximation"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-4 (induced by source paper "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"):
  with members:
    - "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"
    - "separating a voronoi diagram"
    - "approximating k median via pseudo approximation"
    - "improved spectral norm bounds for clustering"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-5 (induced by source paper "local search yields a ptas for k means in doubling metrics"):
  with members:
    - "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"
    - "the unreasonable success of local search geometric optimization"
    - "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-6 (induced by source paper "better guarantees for k means and euclidean k median by primal dual algorithms"):
  with members:
    - "local search yields a ptas for k means in doubling metrics"
    - "improved and simplified inapproximability for k means"
    - "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"
    - "simpler analyses of local search algorithms for facility location"
    - "k means requires exponentially many iterations even in the plane"
    - "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem"

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)