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 "polylogarithmic approximation algorithms for weighted mathcal f deletion problems"):
  Abstract: For a family of graphs $\cal F$, the canonical Weighted $\cal F$ Vertex Deletion problem is defined as follows: given an $n$-vertex undirected graph $G$ and a weight function $w: V(G)\rightarrow\mathbb{R}$, find a minimum weight subset $S\subseteq V(G)$ such that $G-S$ belongs to $\cal F$. We devise a recursive scheme to obtain $O(\log^{O(1)}n)$-approximation algorithms for such problems, building upon the classic technique of finding balanced separators in a graph. Roughly speaking, our scheme applies to problems where an optimum solution $S$, together with a well-structured set $X$, form a balanced separator of $G$. We obtain the first $O(\log^{O(1)}n)$-approximation algorithms for the following problems. #R##N#* We give an $O(\log^2n)$-factor approximation algorithm for Weighted Chordal Vertex Deletion (WCVD), the vertex deletion problem to the family of chordal graphs. On the way, we also obtain a constant factor approximation algorithm for Multicut on chordal graphs. #R##N#* We give an $O(\log^3n)$-factor approximation algorithm for Weighted Distance Hereditary Vertex Deletion (WDHVD). This is the vertex deletion problem to the family of distance hereditary graphs, or equivalently, the family of graphs of rankwidth 1. #R##N#Our methods also allow us to obtain in a clean fashion a $O(\log^{1.5}n)$-approximation algorithm for the Weighted $\cal F$ Vertex Deletion problem when $\cal F$ is a minor closed family excluding at least one planar graph. For the unweighted version of the problem constant factor approximation algorithms are were known~[Fomin et al., FOCS~2012], while for the weighted version considered here an $O(\log n \log\log n)$-approximation algorithm follows from~[Bansal et al., SODA~2017]. We believe that our recursive scheme can be applied to obtain $O(\log^{O(1)}n)$-approximation algorithms for many other problems as well.

Includes members:
  Member "feedback vertex set inspired kernel for chordal vertex deletion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal graphs")
  Member "approximation and kernelization for chordal vertex deletion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal graphs")
  Member "bidimensionality and kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hitting topological minors is fpt")
  Member "lp based robust algorithms for noisy minor free and bounded treewidth graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "covering vectors by spaces in perturbed graphic matroids and their duals")
  Member "bidimensionality and geometric graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "planar graphs have bounded queue number")
  Member "hitting forbidden minors approximation and kernelization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structural parameterization for graph deletion problems over data streams")
  Member "bidimensionality and eptas":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved in approximability bounds for d scattered set")
  Member "hitting diamonds and growing cacti":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "losing treewidth by separating subsets")

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)