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: "optimal time bounds for approximate clustering"
  Abstract: Clustering is a fundamental problem in unsupervised learning, and has been studied widely both as a problem of learning mixture models and as an optimization problem. In this paper, we study clustering with respect the emph{k-median} objective function, a natural formulation of clustering in which we attempt to minimize the average distance to cluster centers. One of the main contributions of this paper is a simple but powerful sampling technique that we call emph{successive sampling} that could be of independent interest. We show that our sampling procedure can rapidly identify a small set of points (of size just O(klog{n/k})) that summarize the input points for the purpose of clustering. Using successive sampling, we develop an algorithm for the k-median problem that runs in O(nk) time for a wide range of values of k and is guaranteed, with high probability, to return a solution with cost at most a constant factor times optimal. We also establish a lower bound of Omega(nk) on any randomized constant-factor approximation algorithm for the k-median problem that succeeds with even a negligible (say 1/100) probability. Thus we establish a tight time bound of Theta(nk) for the k-median problem for a wide range of values of k. The best previous upper bound for the problem was O(nk), where the O-notation hides polylogarithmic factors in n and k. The best previous lower bound of O(nk) applied only to deterministic k-median algorithms. While we focus our presentation on the k-median objective, all our upper bounds are valid for the k-means objective as well. In this context our algorithm compares favorably to the widely used k-means heuristic, which requires O(nk) time for just one iteration and provides no useful approximation guarantees.

Connected through hyperedge HE-1 (induced by source paper "layered sampling for robust optimization problems"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "iterative least trimmed squares for mixed linear regression"
    - "a practical algorithm for distributed clustering and outlier detection"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "efficient algorithms for outlier robust regression"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "sobol tensor trains for global sensitivity analysis"

Connected through hyperedge HE-2 (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-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 "proportionally fair clustering"):
  with members:
    - "fair algorithms for clustering"
    - "privacy preserving clustering with constraints"
    - "markets for public decision making"
    - "fair clustering through fairlets"
    - "fair allocation of indivisible public goods"
    - "on fairness and calibration"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-5 (induced by source paper "a practical framework for solving center based clustering with outliers"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "distributed partial clustering"
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "the non uniform k center problem"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-6 (induced by source paper "a practical algorithm for distributed clustering and outlier detection"):
  with members:
    - "distributed partial clustering"
    - "communication optimal distributed clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "scalable k means"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-7 (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"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "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"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-8 (induced by source paper "metric random matchings with applications"):
  with members:
    - "metric 1 median selection query complexity vs approximation ratio"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "a lower bound for metric 1 median selection"
    - "fast approximation of centrality"

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)