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: "performance of johnson lindenstrauss transform for k means and k medians clustering"
  Abstract: Consider an instance of Euclidean $k$-means or $k$-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of $(1+\varepsilon)$ under a projection onto a random $O(\log(k / \varepsilon) / \varepsilon^2)$-dimensional subspace. Further, the cost of every clustering is preserved within $(1+\varepsilon)$. More generally, our result applies to any dimension reduction map satisfying a mild sub-Gaussian-tail condition. Our bound on the dimension is nearly optimal. Additionally, our result applies to Euclidean $k$-clustering with the distances raised to the $p$-th power for any constant $p$. #R##N#For $k$-means, our result resolves an open problem posed by Cohen, Elder, Musco, Musco, and Persu (STOC 2015); for $k$-medians, it answers a question raised by Kannan.

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"
    - "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"
    - "lectures on randomized numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "fair clustering with multiple colors"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "privacy preserving clustering with constraints"
    - "constant factor fpt approximation for capacitated k median"
    - "a unified framework for clustering constrained data without locality property"

Connected through hyperedge HE-3 (induced by source paper "individual fairness for k clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "r enyi fair inference"
    - "a survey on bias and fairness in machine learning"
    - "fairness in clustering with multiple sensitive attributes"
    - "a center in your neighborhood fairness in facility location"
    - "scalable fair clustering"
    - "clustering without over representation"
    - "on the cost of essentially fair clusterings"

Connected through hyperedge HE-4 (induced by source paper "explainable k means and k medians clustering"):
  with members:
    - "individual fairness for k clustering"
    - "coresets for clustering with fairness constraints"
    - "same cluster querying for overlapping clusters"
    - "online k means clustering"
    - "fair algorithms for clustering"
    - "unexpected effects of online k means clustering"
    - "sequential no substitution k median clustering"
    - "scalable fair clustering"

Connected through hyperedge HE-5 (induced by source paper "streaming balanced clustering"):
  with members:
    - "a constant parameterized approximation for hard capacitated k means"
    - "constant factor fpt approximation for capacitated k median"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "relative error tensor low rank approximation"
    - "clustering high dimensional dynamic data streams"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "new frameworks for offline and streaming coreset constructions"
    - "low rank approximation with entrywise ell_1 norm error"

Connected through hyperedge HE-6 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"
    - "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"

Connected through hyperedge HE-7 (induced by source paper "strong coresets for subspace approximation and k median in nearly linear time"):
  with members:
    - "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"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "on the sensitivity of shape fitting problems"

Connected through hyperedge HE-8 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "improved coresets for kernel density estimates"
    - "coresets for kernel regression"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "estimating continuous distributions in bayesian classifiers"
    - "union of random minkowski sums and network vulnerability analysis"
    - "geometric inference on kernel density estimates"

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)