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 "capacitated covering problems in geometric spaces"):
  Abstract: In this article, we consider the following capacitated covering problem. We are given a set $P$ of $n$ points and a set $\mathcal{B}$ of balls from some metric space, and a positive integer $U$ that represents the capacity of each of the balls in $\mathcal{B}$. We would like to compute a subset $\mathcal{B}' \subseteq \mathcal{B}$ of balls and assign each point in $P$ to some ball in $\mathcal{B}$ that contains it, such that the number of points assigned to any ball is at most $U$. The objective function that we would like to minimize is the cardinality of $\mathcal{B}$. #R##N#We consider this problem in arbitrary metric spaces as well as Euclidean spaces of constant dimension. In the metric setting, even the uncapacitated version of the problem is hard to approximate to within a logarithmic factor. In the Euclidean setting, the best known approximation guarantee in dimensions $3$ and higher is logarithmic in the number of points. Thus we focus on obtaining "bi-criteria" approximations. In particular, we are allowed to expand the balls in our solution by some factor, but optimal solutions do not have that flexibility. Our main result is that allowing constant factor expansion of the input balls suffices to obtain constant approximations for these problems. In fact, in the Euclidean setting, only $(1+\epsilon)$ factor expansion is sufficient for any $\epsilon > 0$, with the approximation factor being a polynomial in $1/\epsilon$. We obtain these results using a unified scheme for rounding the natural LP relaxation; this scheme may be useful for other capacitated covering problems. We also complement these bi-criteria approximations by obtaining hardness of approximation results that shed light on our understanding of these problems.

Includes members:
  Member "replica placement on bounded treewidth graphs":
    also connected through other hyperedges:
      (none)
  Member "iterative partial rounding for vertex cover with hard capacities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a constant factor approximation for capacitated min max tree cover")
  Member "capacitated dominating set on planar graphs":
    also connected through other hyperedges:
      (none)
  Member "tight algorithms for vertex cover with hard capacities on multigraphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a constant factor approximation for capacitated min max tree cover")
  Member "on uniform capacitated k median beyond the natural lp relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair clustering with multiple colors")
  Member "lp based algorithms for capacitated facility location":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "easy capacitated facility location problems with connections to lot sizing")
  Member "analytical approach to parallel repetition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an improved algorithm for dynamic set cover")
  Member "lp rounding for k centers with non uniform hard capacities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.DS(Data Structures and Algorithms)