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 requires exponentially many iterations even in the plane"
  Abstract: The k-means algorithm is a well-known method for partitioning n points that lie in the d-dimensional space into k clusters. Its main features are simplicity and speed in practice. Theoretically, however, the best known upper bound on its running time (i.e. O(n^{kd})) can be exponential in the number of points. Recently, Arthur and Vassilvitskii [3] showed a super-polynomial worst-case analysis, improving the best known lower bound from \Omega(n) to 2^{\Omega(\sqrt{n})} with a construction in d=\Omega(\sqrt{n}) dimensions. In [3] they also conjectured the existence of superpolynomial lower bounds for any d >= 2. #R##N#Our contribution is twofold: we prove this conjecture and we improve the lower bound, by presenting a simple construction in the plane that leads to the exponential lower bound 2^{\Omega(n)}.

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 "computing optimal assignments in linear time for approximate graph matching"):
  with members:
    - "a survey on graph kernels"
    - "on valid optimal assignment kernels and applications to graph classification"

Connected through hyperedge HE-3 (induced by source paper "on euclidean k means clustering with alpha center proximity"):
  with members:
    - "exact algorithms and lower bounds for stable instances of euclidean k means"
    - "approximation schemes for clustering with outliers"
    - "clustering stable instances of euclidean 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"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "k center clustering under perturbation resilience"
    - "computational feasibility of clustering under clusterability assumptions"

Connected through hyperedge HE-4 (induced by source paper "information theoretical clustering is hard to approximate"):
  with members:
    - "approximation algorithms for clustering via weighted impurity measures"
    - "information distilling quantizers"
    - "greedy merge degrading has optimal power law"
    - "the hardness of approximation of euclidean k means"
    - "channel upgradation for non binary input alphabets and macs"
    - "how to construct polar codes"
    - "quantization of binary input discrete memoryless channels"
    - "approximation algorithms for bregman co clustering and tensor clustering"

Connected through hyperedge HE-5 (induced by source paper "data driven clustering via parameterized lloyd s families"):
  with members:
    - "an online hierarchical algorithm for extreme clustering"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "geometric median in nearly linear time"
    - "going deeper with convolutions"
    - "deep domain confusion maximizing for domain invariance"
    - "an improved approximation for k median and positive correlation in budgeted optimization"

Connected through hyperedge HE-6 (induced by source paper "fast k means based on knn graph"):
  with members:
    - "boost k means"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "approximate nearest neighbor search on high dimensional data experiments analyses and improvement v1 0"
    - "yfcc100m the new data in multimedia research"
    - "fast approximate k means via cluster closures"
    - "scalable k means"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "fast exact k means k medians and bregman divergence clustering in 1d"):
  with members:
    - "the hardness of approximation of euclidean k means"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"

Connected through hyperedge HE-8 (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"
    - "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"

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)