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: "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
  Abstract: Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular $k$-center variant which, given a set $S$ of points from some metric space and a parameter $k 0$, the algorithms yield solutions whose approximation ratios are a mere additive term $\epsilon$ away from those achievable by the best known polynomial-time sequential algorithms, a result that substantially improves upon the state of the art. Our algorithms are rather simple and adapt to the intrinsic complexity of the dataset, captured by the doubling dimension $D$ of the metric space. Specifically, our analysis shows that the algorithms become very space-efficient for the important case of small (constant) $D$. These theoretical results are complemented with a set of experiments on real-world and synthetic datasets of up to over a billion points, which show that our algorithms yield better quality solutions over the state of the art while featuring excellent scalability, and that they also lend themselves to sequential implementations much faster than existing ones.

Connected through hyperedge HE-1 (induced by source paper "coreset based strategies for robust center type problems"):
  with members:
    - "a general coreset based approach to diversity maximization under matroid constraints"
    - "small space stream summary for matroid center"
    - "generalized center problems with outliers"
    - "a lottery model for center type problems with outliers"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "matroid and knapsack center problems"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-2 (induced by source paper "scalable distributed approximation of internal measures for clustering evaluation"):
  with members:
    - "accurate mapreduce algorithms for k median and k means in general metric spaces"
    - "clustering uncertain graphs"
    - "k means for streaming and distributed big sparse data"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "communication steps for parallel query processing"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-3 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  with members:
    - "beyond worst case analysis"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "the non uniform k center problem"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "approximation and streaming algorithms for projective clustering via random projections"

Connected through hyperedge HE-4 (induced by source paper "a practical framework for solving center based clustering with outliers"):
  with members:
    - "beyond worst case analysis"
    - "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"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-5 (induced by source paper "a composable coreset for k center in doubling metrics"):
  with members:
    - "greedy strategy works for k center clustering with outliers and coreset construction"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "efficient parallel algorithms for k center clustering"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "randomized composable core sets for distributed submodular maximization"
    - "approximating k median via pseudo approximation"
    - "fast clustering using mapreduce"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-6 (induced by source paper "greedy strategy works for k center clustering with outliers and coreset construction"):
  with members:
    - "a composable coreset for k center in doubling metrics"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "the non uniform k center problem"
    - "clustering is difficult only when it does not matter"
    - "fast construction of nets in low dimensional metrics and their applications"

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.DC(Distributed, Parallel, and Cluster Computing)