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: "scalable k means"
  Abstract: Over half a century old and showing no signs of aging, k-means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k-means is crucial for obtaining a good final solution. The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution. A major downside of the k-means++ is its inherent sequential nature, which limits its applicability to massive data: one must make k passes over the data to find a good initial set of centers. In this work we show how to drastically reduce the number of passes needed to obtain, in parallel, a good initialization. This is unlike prevailing efforts on parallelizing k-means that have mostly focused on the post-initialization phases of k-means. We prove that our proposed initialization algorithm k-means|| obtains a nearly optimal solution after a logarithmic number of passes, and then show that in practice a constant number of passes suffices. Experimental evaluation on real-world large-scale data demonstrates that k-means|| outperforms k-means++ in both sequential and parallel settings.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-3 (induced by source paper "federated clustering via matrix factorization models from model averaging to gradient sharing"):
  with members:
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "agnostic federated learning"
    - "clustering by orthogonal nmf model and non convex penalty optimization"
    - "secure federated matrix factorization"
    - "federated machine learning concept and applications"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (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"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "distributed bayesian matrix decomposition for big data mining and clustering"):
  with members:
    - "bayesian joint matrix decomposition for data integration with heterogeneous noise"
    - "mllib machine learning in apache spark"
    - "a survey on multi view learning"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "non negative sparse coding"

Connected through hyperedge HE-7 (induced by source paper "clustering and classification with non existence attributes a sentenced discrepancy measure based technique"):
  with members:
    - "clustering with missing features a penalized dissimilarity measure based approach"

Connected through hyperedge HE-8 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "distributed and streaming linear programming in low dimensions"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

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.DB(Databases)