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: "parallel approximation algorithms for facility location problems"
  Abstract: This paper presents the design and analysis of parallel approximation algorithms for facility-location problems, including $\NC$ and $\RNC$ algorithms for (metric) facility location, $k$-center, $k$-median, and $k$-means. These problems have received considerable attention during the past decades from the approximation algorithms community, concentrating primarily on improving the approximation guarantees. In this paper, we ask, is it possible to parallelize some of the beautiful results from the sequential setting? #R##N#Our starting point is a small, but diverse, subset of results in approximation algorithms for facility-location problems, with a primary goal of developing techniques for devising their efficient parallel counterparts. We focus on giving algorithms with low depth, near work efficiency (compared to the sequential versions), and low cache complexity. Common in algorithms we present is the idea that instead of picking only the most cost-effective element, we make room for parallelism by allowing a small slack (e.g., a $(1+\vareps)$ factor) in what can be selected---then, we use a clean-up step to ensure that the behavior does not deviate too much from the sequential steps. All the algorithms we developed are ``cache efficient'' in that the cache complexity is bounded by $O(w/B)$, where $w$ is the work in the EREW model and $B$ is the block size.

Connected through hyperedge HE-1 (induced by source paper "sets clustering"):
  with members:
    - "introduction to coresets accurate coresets"
    - "tight sensitivity bounds for smaller coresets"
    - "k means clustering of lines for big data"
    - "on coresets for logistic regression"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "k means for streaming and distributed big sparse data"
    - "improved and simplified inapproximability for k means"

Connected through hyperedge HE-2 (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"
    - "a bi criteria approximation algorithm for k means"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-3 (induced by source paper "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"):
  with members:
    - "the power of local search for clustering"
    - "local search yields a ptas for k means in doubling metrics"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "separating a voronoi diagram"
    - "approximating k median via pseudo approximation"
    - "improved spectral norm bounds for clustering"

Connected through hyperedge HE-4 (induced by source paper "the power of local search for clustering"):
  with members:
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "separating a voronoi diagram"
    - "approximating k median via pseudo approximation"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "efficient parallel algorithms for k center clustering"):
  with members:
    - "a practical parallel algorithm for diameter approximation of massive weighted graphs"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "scalable k means"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-6 (induced by source paper "parallel metric tree embedding based on an algebraic view on moore bellman ford"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "efficient construction of probabilistic tree embeddings"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "an almost tight distributed algorithm for computing single source shortest paths"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "fast partial distance estimation and applications"

Connected through hyperedge HE-7 (induced by source paper "scalable facility location for massive graphs on pregel like systems"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "densest subgraph in streaming and mapreduce"
    - "social content matching in mapreduce"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-8 (induced by source paper "a distributed approximation algorithm for the metric uncapacitated facility location problem in the congest model"):
  with members:
    - "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)