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: "a constant factor bi criteria approximation guarantee for k means"
  Abstract: This paper studies the $k$-means++ algorithm for clustering as well as the class of $D^\ell$ sampling algorithms to which $k$-means++ belongs. It is shown that for any constant factor $\beta > 1$, selecting $\beta k$ cluster centers by $D^\ell$ sampling yields a constant-factor approximation to the optimal clustering with $k$ centers, in expectation and without conditions on the dataset. This result extends the previously known $O(\log k)$ guarantee for the case $\beta = 1$ to the constant-factor bi-criteria regime. It also improves upon an existing constant-factor bi-criteria result that holds only with constant probability.

Connected through hyperedge HE-1 (induced by source paper "simple and sharp analysis of k means"):
  with members:
    - "noisy greedy and not so greedy k means"
    - "submodular optimization in the mapreduce model"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"
    - "a new framework for distributed submodular maximization"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "a simple d 2 sampling based ptas for k means and other clustering problems"

Connected through hyperedge HE-2 (induced by source paper "k means few more steps yield constant approximation"):
  with members:
    - "a fast approximation scheme for low dimensional 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"
    - "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-3 (induced by source paper "noisy greedy and not so greedy k means"):
  with members:
    - "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"
    - "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-4 (induced by source paper "accurate mapreduce algorithms for k median and k means in general metric spaces"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "coresets and sketches"
    - "space round tradeoffs for mapreduce computations"
    - "fast clustering using mapreduce"
    - "a unified framework for approximating and clustering data"
    - "simpler analyses of local search algorithms for facility location"

Connected through hyperedge HE-5 (induced by source paper "clustering small samples with quality guarantees adaptivity with one2all pps"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "multi objective weighted sampling"
    - "the hardness of approximation of euclidean k means"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "optimal time bounds for approximate clustering"
    - "a unified framework for approximating and clustering data"

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.LG(Machine Learning)