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 "approximation and parameterized algorithms for geometric independent set with shrinking"):
  Abstract: Consider the Maximum Weight Independent Set problem for rectangles: given a family of weighted axis-parallel rectangles in the plane, find a maximum-weight subset of non-overlapping rectangles. The problem is notoriously hard both in the approximation and in the parameterized setting. The best known polynomial-time approximation algorithms achieve super-constant approximation ratios [Chalermsook and Chuzhoy, SODA 2009; Chan and Har-Peled, Discrete & Comp. Geometry 2012], even though there is a $(1+\epsilon)$-approximation running in quasi-polynomial time [Adamaszek and Wiese, FOCS 2013; Chuzhoy and Ene, FOCS 2016]. When parameterized by the target size of the solution, the problem is $\mathsf{W}[1]$-hard even in the unweighted setting [Marx, FOCS 2007]. #R##N#To achieve tractability, we study the following shrinking model: one is allowed to shrink each input rectangle by a multiplicative factor $1-\delta$ for some fixed $\delta>0$, but the performance is still compared against the optimal solution for the original, non-shrunk instance. We prove that in this regime, the problem admits an EPTAS with running time $f(\epsilon,\delta)\cdot n^{\mathcal{O}(1)}$, and an FPT algorithm with running time $f(k,\delta)\cdot n^{\mathcal{O}(1)}$, in the setting where a maximum-weight solution of size at most $k$ is to be computed. This improves and significantly simplifies a PTAS given earlier for this problem [Adamaszek et al., APPROX 2015], and provides the first parameterized results for the shrinking model. Furthermore, we explore kernelization in the shrinking model, by giving efficient kernelization procedures for several variants of the problem when the input rectangles are squares.

Includes members:
  Member "subexponential parameterized algorithms for planar and apex minor free graphs via low treewidth pattern covering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight bounds for planar strongly connected steiner subgraph with fixed number of terminals and extensions")
  Member "on approximating maximum independent set of rectangles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "independent sets of dynamic rectangles algorithms and experiments")
  Member "optimal parameterized algorithms for planar facility location problems using voronoi diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grundy coloring friends half graphs bicliques")
  Member "polynomial kernels for weighted problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal graphs")
  Member "quasi polynomial time approximation scheme for sparse subsets of polygons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient approximation schemes for uniform cost clustering problems in planar graphs")
  Member "a qptas for maximum weight independent set of polygons with polylogarithmically many vertices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quasi polynomial time approximation schemes for packing and covering problems in planar graphs")
  Member "approximation schemes for maximum weight independent set of rectangles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "independent sets of dynamic rectangles algorithms and experiments")
  Member "approximation algorithms for maximum independent set of pseudo disks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "independent sets of dynamic rectangles algorithms and experiments")

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)