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 "meta kernelization using well structured modulators"):
  Abstract: Kernelization investigates exact preprocessing algorithms with performance guarantees. The most prevalent type of parameters used in kernelization is the solution size for optimization problems; however, also structural parameters have been successfully used to obtain polynomial kernels for a wide range of problems. Many of these parameters can be defined as the size of a smallest modulator of the given graph into a fixed graph class (i.e., a set of vertices whose deletion puts the graph into the graph class). Such parameters admit the construction of polynomial kernels even when the solution size is large or not applicable. This work follows up on the research on meta-kernelization frameworks in terms of structural parameters. #R##N#We develop a class of parameters which are based on a more general view on modulators: instead of size, the parameters employ a combination of rank-width and split decompositions to measure structure inside the modulator. This allows us to lift kernelization results from modulator-size to more general parameters, hence providing smaller kernels. We show (i) how such large but well-structured modulators can be efficiently approximated, (ii) how they can be used to obtain polynomial kernels for any graph problem expressible in Monadic Second Order logic, and (iii) how they allow the extension of previous results in the area of structural meta-kernelization.

Includes members:
  Member "bidimensionality and kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hitting topological minors is fpt")
  Member "solving problems on graphs of high rank width":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backdoors into heterogeneous classes of sat and csp")
  Member "kernelization using structural parameters on sparse graph classes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the parameterized complexity of reconfiguration of connected dominating sets")
  Member "meta kernelization with structural parameters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "measuring what matters a hybrid approach to dynamic programming with treewidth")
  Member "kernelization lower bounds by cross composition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal graphs")
  Member "linear kernels and single exponential algorithms via protrusion decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hitting topological minors is fpt")
  Member "preprocessing subgraph and minor problems when does a small vertex cover help":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "measuring what matters a hybrid approach to dynamic programming with treewidth")
  Member "kernel bounds for path and cycle problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parameterized pre coloring extension and list coloring 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)