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: "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.

Connected through hyperedge HE-1 (induced by source paper "an aposteriorical clusterability criterion for k means and simplicity of clustering"):
  with members:
    - "on the discrepancy between kleinberg s clustering axioms and k means clustering algorithm behavior"
    - "an effective and efficient approach for clusterability evaluation"
    - "computational feasibility of clustering under clusterability assumptions"
    - "incremental clustering the case for extra clusters"
    - "clustering with spectral norm and the k means algorithm"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "approximating optimization problems using eas on scale free networks"):
  with members:
    - "greed is good for deterministic scale free networks"
    - "algorithmic complexity of power law networks"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"

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)