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: "k means has polynomial smoothed complexity"
  Abstract: The k-means method is one of the most widely used clustering algorithms, drawing its popularity from its speed in practice. Recently, however, it was shown to have exponential worst-case running time. In order to close the gap between practical performance and theoretical analysis, the k-means method has been studied in the model of smoothed analysis. But even the smoothed analyses so far are unsatisfactory as the bounds are still super-polynomial in the number n of data points. #R##N#In this paper, we settle the smoothed running time of the k-means method. We show that the smoothed number of iterations is bounded by a polynomial in n and 1/\sigma, where \sigma is the standard deviation of the Gaussian perturbations. This means that if an arbitrary input data set is randomly perturbed, then the k-means method will run in expected polynomial time on that input set.

Connected through hyperedge HE-1 (induced by source paper "analysis of ward s method"):
  with members:
    - "metric perturbation resilience"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "a cost function for similarity based hierarchical clustering"
    - "improved and simplified inapproximability for k means"
    - "computational feasibility of clustering under clusterability assumptions"
    - "the hardness of approximation of euclidean k means"
    - "analysis of agglomerative clustering"

Connected through hyperedge HE-2 (induced by source paper "a framework for robust realistic geometric computations"):
  with members:
    - "chirotopes of random points in space are realizable on a small integer grid"
    - "smoothed analysis of order types"
    - "optimal curve straightening is exists mathbb r complete"
    - "beyond worst case analysis"
    - "the complexity of drawing a graph in a polygonal region"
    - "on order types of random point sets"
    - "on the complexity of minimum link path problems"
    - "recognition and complexity of point visibility graphs"

Connected through hyperedge HE-3 (induced by source paper "differentially private k means with constant multiplicative error"):
  with members:
    - "practical locally private heavy hitters"
    - "clustering small samples with quality guarantees adaptivity with one2all pps"
    - "heavy hitters and the structure of local privacy"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "simultaneous private learning of multiple concepts"
    - "k means for streaming and distributed big sparse data"

Connected through hyperedge HE-4 (induced by source paper "the well tempered lasso"):
  with members:
    - "smoothed analysis of the condition numbers and growth factors of matrices"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-5 (induced by source paper "theoretical analysis of the k means algorithm a survey"):
  with members:
    - "the hardness of approximation of euclidean k means"
    - "a unified framework for approximating and clustering data"
    - "clustering with spectral norm and the k means algorithm"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "learning gaussian mixtures with arbitrary separation"
    - "improved smoothed analysis of the k means method"
    - "k means requires exponentially many iterations even in the plane"

Connected through hyperedge HE-6 (induced by source paper "cooperative channel estimation for coordinated transmission with limited backhaul"):
  with members:
    - "csit sharing over finite capacity backhaul for spatial interference alignment"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "robust monotonic optimization framework for multicell miso systems"
    - "uplink comp under a constrained backhaul and imperfect channel knowledge"
    - "adaptive limited feedback for sum rate maximizing beamforming in cooperative multicell systems"
    - "distributed compression for the uplink of a backhaul constrained coordinated cellular network"

Connected through hyperedge HE-7 (induced by source paper "a machine learning approach to predicting the smoothed complexity of sorting algorithms"):
  with members:
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-8 (induced by source paper "the madlib analytics library or mad skills the sql"):
  with members:
    - "towards a unified architecture for in rdbms analytics"
    - "graphlab a new framework for parallel machine learning"

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)