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 "subexponential parameterized algorithms and kernelization on almost chordal graphs"):
  Abstract: We study the algorithmic properties of the graph class Chordal-ke, that is, graphs that can be turned into a chordal graph by adding at most k edges or, equivalently, the class of graphs of fill-in at most k. We discover that a number of fundamental intractable optimization problems being parameterized by k admit subexponential algorithms on graphs from Chordal-ke. We identify a large class of optimization problems on Chordal-ke that admit algorithms with the typical running time 2^{O(\sqrt{k}\log k)}\cdot n^{O(1)}. Examples of the problems from this class are finding an independent set of maximum weight, finding a feedback vertex set or an odd cycle transversal of minimum weight, or the problem of finding a maximum induced planar subgraph. On the other hand, we show that for some fundamental optimization problems, like finding an optimal graph coloring or finding a maximum clique, are FPT on Chordal-ke when parameterized by k but do not admit subexponential in k algorithms unless ETH fails. Besides subexponential time algorithms, the class of Chordal-ke graphs appears to be appealing from the perspective of kernelization (with parameter k). While it is possible to show that most of the weighted variants of optimization problems do not admit polynomial in k kernels on Chordal-ke graphs, this does not exclude the existence of Turing kernelization and kernelization for unweighted graphs. In particular, we construct a polynomial Turing kernel for Weighted Clique on Chordal-ke graphs. For (unweighted) Independent Set we design polynomial kernels on two interesting subclasses of Chordal-ke, namely, Interval-ke and Split-ke graphs.

Includes members:
  Member "a complexity dichotomy for hitting connected minors on bounded treewidth graphs the chair and the banner draw the boundary":
    also connected through other hyperedges:
      (none)
  Member "feedback vertex set inspired kernel for chordal vertex deletion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a polynomial kernel for 3 leaf power deletion")
  Member "approximation and kernelization for chordal vertex deletion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structural parameterizations with modulator oblivion")
  Member "polynomial kernels for weighted problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized algorithms and data reduction for the short secluded s t path problem")
  Member "linear recognition of almost interval graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slightly superexponential parameterized problems")
  Member "large induced subgraphs via triangulations and cmso":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hitting topological minors is fpt")
  Member "data reduction for graph coloring problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a turing kernelization dichotomy for structural parameterizations of mathcal f minor free deletion")
  Member "interval deletion is fixed parameter tractable":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hitting topological minors is fpt")

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)