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 bi criteria approximation algorithm for k means"
  Abstract: We consider the classical $k$-means clustering problem in the setting bi-criteria approximation, in which an algoithm is allowed to output $\beta k > k$ clusters, and must produce a clustering with cost at most $\alpha$ times the to the cost of the optimal set of $k$ clusters. We argue that this approach is natural in many settings, for which the exact number of clusters is a priori unknown, or unimportant up to a constant factor. We give new bi-criteria approximation algorithms, based on linear programming and local search, respectively, which attain a guarantee $\alpha(\beta)$ depending on the number $\beta k$ of clusters that may be opened. Our gurantee $\alpha(\beta)$ is always at most $9 + \epsilon$ and improves rapidly with $\beta$ (for example: $\alpha(2)<2.59$, and $\alpha(3) < 1.4$). Moreover, our algorithms have only polynomial dependence on the dimension of the input data, and so are applicable in high-dimensional settings.

Connected through hyperedge HE-1 (induced by source paper "low rank approximation from communication complexity"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "parameterized low rank binary matrix approximation"
    - "relative error tensor low rank approximation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "low rank passthrough neural networks"
    - "greedy bi criteria approximations for k medians and k means"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-2 (induced by source paper "robust communication optimal distributed clustering algorithms"):
  with members:
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "communication optimal distributed clustering"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "data driven resource allocation for distributed learning"
    - "k center clustering under perturbation resilience"

Connected through hyperedge HE-3 (induced by source paper "a fast approximation scheme for low dimensional k means"):
  with members:
    - "tight analysis of a multiple swap heuristic for budgeted red blue median"
    - "new frameworks for offline and streaming coreset constructions"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "the hardness of approximation of euclidean k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"

Connected through hyperedge HE-4 (induced by source paper "a constant factor bi criteria approximation guarantee for k means"):
  with members:
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-5 (induced by source paper "k center clustering under perturbation resilience"):
  with members:
    - "improved and simplified inapproximability for k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "decompositions of triangle dense graphs"
    - "on the practically interesting instances of maxcut"
    - "improved spectral norm bounds for clustering"
    - "beyond worst case analysis in private singular vector computation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-6 (induced by source paper "on variants of k means clustering"):
  with members:
    - "the hardness of approximation of euclidean k means"
    - "separating a voronoi diagram"
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "simpler analyses of local search algorithms for facility location"

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)