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 "one size fits all effectiveness of local search on structured data"):
  Abstract: In this paper, we analyze the performance of a simple and standard Local Search algorithm for clustering on well behaved data. Since the seminal paper by Ostrovsky, Rabani, Schulman and Swamy [FOCS 2006], much progress has been made to characterize real-world instances. We distinguish the three main definitions -- Distribution Stability (Awasthi, Blum, Sheffet, FOCS 2010) -- Spectral Separability (Kumar, Kannan, FOCS 2010) -- Perturbation Resilience (Bilu, Linial, ICS 2010) We show that Local Search performs well on the instances with the aforementioned stability properties. Specifically, for the $k$-means and $k$-median objective, we show that Local Search exactly recovers the optimal clustering if the dataset is $3+\varepsilon$-perturbation resilient, and is a PTAS for distribution stability and spectral separability. This implies the first PTAS for instances satisfying the spectral separability condition. For the distribution stability condition we also go beyond previous work by showing that the clustering output by the algorithm and the optimal clustering are very similar. This is a significant step toward understanding the success of Local Search heuristics in clustering applications and supports the legitimacy of the stability conditions: They characterize some of the structure of real-world instances that make Local Search a popular heuristic.

Includes members:
  Member "the power of local search for clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an o log 1 5 n log log n approximation algorithm for mean isoperimetry and robust k means")
  Member "tight analysis of a multiple swap heuristic for budgeted red blue median":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a fast approximation scheme for low dimensional k means")
  Member "local search yields a ptas for k means in doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "k means few more steps yield constant approximation")
  Member "on variants of k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k means few more steps yield constant approximation")
  Member "k center clustering under perturbation resilience":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi bandit optimization in the dispersed setting")
  Member "approximation and streaming algorithms for projective clustering via random projections":
    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 "an improved approximation for k median and positive correlation in budgeted optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "individual fairness for k clustering")
  Member "dimensionality reduction for k means clustering and low rank approximation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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.DS(Data Structures and Algorithms)