human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  Abstract: In this paper, we revisit the Minimum Enclosing Ball (MEB) problem and its robust version, MEB with outliers, in Euclidean space $\mathbb{R}^d$. Though the problem has been extensively studied before, most of the existing algorithms need at least linear time (in the number of input points $n$ and the dimensionality $d$) to achieve a $(1+\epsilon)$-approximation. Motivated by some recent developments on beyond worst-case analysis, we introduce the notion of stability for MEB (with outliers), which is natural and easy to understand. Under the stability assumption, we present two sampling algorithms for computing approximate MEB with sample complexities independent of the number of input points; further, we achieve the first sub-linear time single-criterion approximation algorithm for the MEB with outliers problem. Our result can be viewed as a new step along the direction of beyond worst-case analysis. We also show that our ideas can be extended to be more general techniques, a novel uniform-adaptive sampling method and a sandwich lemma, for solving the general case of MEB with outliers ({\em i.e.,} without the stability assumption) and the problem of $k$-center clustering with outliers. We achieve sub-linear time bi-criteria approximation algorithms for these problems respectively; the algorithms have sample sizes independent of the number of points $n$ and the dimensionality $d$, which significantly improve the time complexities of existing algorithms. We expect that our technique will be applicable to design sub-linear time algorithms for other shape fitting with outliers problems.

Includes members:
  Member "beyond worst case analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer")
  Member "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coreset based strategies for robust center type problems")
  Member "distributed partial clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast noise removal for k means clustering")
  Member "coresets and sketches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "layered sampling for robust optimization problems")
  Member "locating a small cluster privately":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians")
  Member "the non uniform k center problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "k center clustering under perturbation resilience":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semi bandit optimization in the dispersed setting")
  Member "optimization algorithms for faster computational geometry":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimum enclosing ball revisited stability and sub linear time algorithms")

Question: Please tell me which class the source paper 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.CG(Computational Geometry)