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: "a lower bound for metric 1 median selection"
  Abstract: Consider the problem of finding a point in an n-point metric space with the minimum average distance to all points. We show that this problem has no deterministic $o(n^2)$-query $(4-\Omega(1))$-approximation algorithms.

Connected through hyperedge HE-1 (induced by source paper "metric random matchings with applications"):
  with members:
    - "metric 1 median selection query complexity vs approximation ratio"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "optimal time bounds for approximate clustering"
    - "fast approximation of centrality"

Connected through hyperedge HE-2 (induced by source paper "a las vegas approximation algorithm for metric 1 median selection"):
  with members:
    - "metric 1 median selection query complexity vs approximation ratio"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "optimal time bounds for approximate clustering"
    - "fast approximation of centrality"

Connected through hyperedge HE-3 (induced by source paper "metric 1 median selection with fewer queries"):
  with members:
    - "metric 1 median selection query complexity vs approximation ratio"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"

Connected through hyperedge HE-4 (induced by source paper "metric 1 median selection query complexity vs approximation ratio"):
  with members:
    - "metric 1 median selection with fewer queries"
    - "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "a simple d 2 sampling based ptas for k means and other clustering problems"
    - "optimal time bounds for approximate clustering"
    - "fast approximation of centrality"

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)