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 "metric 1 median selection query complexity vs approximation ratio"):
  Abstract: Consider the problem of finding a point in a metric space $(\{1,2,\ldots,n\},d)$ with the minimum average distance to other points. We show that this problem has no deterministic $o(n^{1+1/(h-1)})$-query $(2h-\Omega(1))$-approximation algorithms for any constant $h\in\mathbb{Z}^+\setminus\{1\}$.

Includes members:
  Member "metric 1 median selection with fewer queries":
    also connected through other hyperedges:
      (none)
  Member "a deterministic sublinear time nonadaptive algorithm for metric 1 median selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metric random matchings with applications")
  Member "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable distributed approximation of internal measures for clustering evaluation")
  Member "a lower bound for metric 1 median selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metric random matchings with applications")
  Member "a simple d 2 sampling based ptas for k means and other clustering problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simple and sharp analysis of k means")
  Member "optimal time bounds for approximate clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "layered sampling for robust optimization problems")
  Member "fast approximation of centrality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic approximate shortest paths and beyond subquadratic and worst case update time")

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.CC(Computational Complexity)