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 "improved local search based approximation algorithm for hard uniform capacitated k median problem"):
  Abstract: In this paper, we study the hard uniform capacitated $k$- median problem using local search heuristic. Obtaining a constant factor approximation for the \ckm problem is open. All the existing solutions giving constant-factor approximation, violate at least one of the cardinality and the capacity constraints. All except Koruplou et al are based on LP-relaxation. #R##N#We give $(3+\epsilon)$ factor approximation algorithm for the problem violating the cardinality by a factor of $8/3 \approx 2.67$. There is a trade-off between the approximation factor and the cardinality violation between our work and the existing work. Koruplou et al gave $(1 + \alpha)$ approximation factor with $(5 + 5/\alpha)$ factor loss in cardinality using local search paradigm. Though the approximation factor can be made arbitrarily small, cardinality loss is at least $5$. #R##N#On the other hand, we improve upon the results in [capkmGijswijtL2013],[capkmshili2014], [Lisoda2016] in terms of factor-loss though the cardinality loss is more in our case. Also, these results are obtained using LP-rounding, some of them being strengthened, whereas local search techniques are simple to apply and have been shown to perform well in practice via empirical studies. #R##N#We extend the result to hard uniform capacitated $k$-median with penalties. To the best of our knowledge, ours is the first result for the problem.

Includes members:
  Member "constant approximation for capacitated k median with 1 epsilon capacity violation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair clustering with multiple colors")
  Member "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad":
    also connected through other hyperedges:
      (none)
  Member "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming balanced clustering")
  Member "an improved approximation algorithm for the hard uniform capacitated k median problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad")
  Member "on uniform capacitated k median beyond the natural lp relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair clustering with multiple colors")
  Member "approximation algorithms for capacitated facility location problem with penalties":
    also connected through other hyperedges:
      (none)
  Member "approximation algorithms for hard capacitated k facility location problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "facility location problem with capacity constraints algorithmic and mechanism design perspectives")
  Member "bi factor approximation algorithms for hard capacitated k median problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reconciliation k median clustering with non polarized representatives")

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)