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 "approximating capacitated k median with 1 epsilon k open facilities"):
  Abstract: In the capacitated $k$-median (\CKM) problem, we are given a set $F$ of facilities, each facility $i \in F$ with a capacity $u_i$, a set $C$ of clients, a metric $d$ over $F \cup C$ and an integer $k$. The goal is to open $k$ facilities in $F$ and connect the clients $C$ to the open facilities such that each facility $i$ is connected by at most $u_i$ clients, so as to minimize the total connection cost. #R##N#In this paper, we give the first constant approximation for \CKM, that only violates the cardinality constraint by a factor of $1+\epsilon$. This generalizes the result of [Li15], which only works for the uniform capacitated case. Moreover, the approximation ratio we obtain is $O\big(\frac{1}{\epsilon^2}\log\frac1\epsilon\big)$, which is an exponential improvement over the ratio of $\exp(O(1/\epsilon^2))$ in [Li15]. The natural LP relaxation for the problem, which almost all previous algorithms for \CKM are based on, has unbounded integrality gap even if $(2-\epsilon)k$ facilities can be opened. We introduce a novel configuration LP for the problem, that overcomes this integrality gap.

Includes members:
  Member "an improved approximation algorithm for the hard uniform capacitated k median problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved local search based approximation algorithm for hard uniform capacitated k median problem")
  Member "on uniform capacitated k median beyond the natural lp relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair clustering with multiple colors")
  Member "an improved approximation for k median and positive correlation in budgeted optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "individual fairness for k clustering")
  Member "an improved approximation algorithm for k median problem using a new factor revealing lp":
    also connected through other hyperedges:
      (none)
  Member "approximation algorithms for hard capacitated k facility location problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "reconciliation k median clustering with non polarized representatives")
  Member "approximating k median via pseudo approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fair correlation clustering")
  Member "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constant factor approximation algorithms for parity constrained facility location problems")

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)