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 "balanced k center clustering when k is a constant"):
  Abstract: The problem of constrained $k$-center clustering has attracted significant attention in the past decades. In this paper, we study balanced $k$-center cluster where the size of each cluster is constrained by the given lower and upper bounds. The problem is motivated by the applications in processing and analyzing large-scale data in high dimension. We provide a simple nearly linear time $4$-approximation algorithm when the number of clusters $k$ is assumed to be a constant. Comparing with existing method, our algorithm improves the approximation ratio and significantly reduces the time complexity. Moreover, our result can be easily extended to any metric space.

Includes members:
  Member "capacitated center problems with two sided bounds and outliers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster balanced clusterings in high dimension")
  Member "approximation algorithms for clustering problems with lower bounds and outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair algorithms for clustering")
  Member "distributed balanced partitioning via linear embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent")
  Member "data driven resource allocation for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster balanced clusterings in high dimension")
  Member "constant factor approximation for capacitated k center with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster balanced clusterings in high dimension")
  Member "fast clustering with lower bounds no customer too far no shop too small":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constant factor approximation algorithms for parity constrained facility location problems")
  Member "lp rounding for k centers with non uniform hard capacities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fair clustering with multiple colors")

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)