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: "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
  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 "k means few more steps yield constant approximation"):
  with members:
    - "a fast approximation scheme for low dimensional k means"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "scalable k means"

Connected through hyperedge HE-2 (induced by source paper "autonomous unknown application filtering and labeling for dl based traffic classifier update"):
  with members:
    - "pointwise convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "coresets for ordered weighted clustering"
    - "k means clustering of lines for big data"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"

Connected through hyperedge HE-4 (induced by source paper "efficient approximation schemes for uniform cost clustering problems in planar graphs"):
  with members:
    - "a polynomial time approximation scheme for facility location on planar graphs"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a constant fpt approximation algorithm for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-6 (induced by source paper "streaming ptas for constrained k means"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "semi supervised algorithms for approximately optimal and accurate clustering"
    - "a unified framework for clustering constrained data without locality property"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "faster algorithms for the constrained k means problem"
    - "the hardness of approximation of euclidean k means"
    - "sampling in space restricted settings"

Connected through hyperedge HE-7 (induced by source paper "noisy greedy and not so greedy k means"):
  with members:
    - "a constant factor bi criteria approximation guarantee for k means"
    - "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"
    - "the hardness of approximation of euclidean k means"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-8 (induced by source paper "a constant parameterized approximation for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "local search yields a ptas for k means in doubling metrics"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "the hardness of approximation of euclidean k means"
    - "on uniform capacitated k median beyond the natural lp relaxation"

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)