human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "kernelization using structural parameters on sparse graph classes"
  Abstract: Meta-theorems for polynomial (linear) kernels have been the subject of intensive research in parameterized complexity. Heretofore, meta-theorems for linear kernels exist on graphs of bounded genus, $H$-minor-free graphs, and $H$-topological-minor-free graphs. To the best of our knowledge, no meta-theorems for polynomial kernels are known for any larger sparse graph classes; e.g., for classes of bounded expansion or for nowhere dense ones. In this paper we prove such meta-theorems for the two latter cases. More specifically, we show that graph problems that have finite integer index (FII) have linear kernels on graphs of bounded expansion when parameterized by the size of a modulator to constant-treedepth graphs. For nowhere dense graph classes, our result yields almost-linear kernels. While our parameter may seem rather strong, we argue that a linear kernelization result on graphs of bounded expansion with a weaker parameter (than treedepth modulator) would fail to include some of the problems covered by our framework. Moreover, we only require the problems to have FII on graphs of constant treedepth. This allows us to prove linear kernels for problems such as Longest Path/Cycle, Exact $s,t$-Path, Treewidth, and Pathwidth, which do not have FII on general graphs (and the first two not even on bounded treewidth graphs).

Connected through hyperedge HE-1 (induced by source paper "on the parameterized complexity of reconfiguration of connected dominating sets"):
  with members:
    - "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"
    - "reconfiguration on nowhere dense graph classes"
    - "polynomial kernels and wideness properties of nowhere dense graph classes"
    - "neighborhood complexity and kernelization for nowhere dense classes of graphs"
    - "reconfiguration of dominating sets"
    - "deciding first order properties of nowhere dense graphs"
    - "the complexity of change"
    - "flip distance between two triangulations of a point set is np complete"

Connected through hyperedge HE-2 (induced by source paper "scalable kernelization for maximum independent sets"):
  with members:
    - "exact algorithms for maximum independent set"
    - "accelerating local search for the maximum independent set problem"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "on the power of simple reductions for the maximum independent set problem"
    - "finding near optimal independent sets at scale"
    - "evaluation of labeling strategies for rotating maps"
    - "parallel graph partitioning for complex networks"
    - "linear time fpt algorithms via network flow"

Connected through hyperedge HE-3 (induced by source paper "algorithmic properties of sparse digraphs"):
  with members:
    - "characterising bounded expansion by neighbourhood complexity"
    - "polynomial kernels and wideness properties of nowhere dense graph classes"
    - "bidimensionality and kernels"
    - "neighborhood complexity and kernelization for nowhere dense classes of graphs"
    - "kernelization and sparseness the case of dominating set"
    - "parameterized complexity of directed steiner tree on sparse graphs"

Connected through hyperedge HE-4 (induced by source paper "being even slightly shallow makes life hard"):
  with members:
    - "kernelization and sparseness the case of dominating set"
    - "solving connectivity problems parameterized by treewidth in single exponential time"
    - "problems parameterized by treewidth tractable in single exponential time a logical approach"
    - "minimum weight triangulation is np hard"

Connected through hyperedge HE-5 (induced by source paper "characterising bounded expansion by neighbourhood complexity"):
  with members:
    - "bidimensionality and kernels"
    - "structural sparsity of complex networks bounded expansion in random models and real world graphs"
    - "kernelization and sparseness the case of dominating set"
    - "deciding first order properties of nowhere dense graphs"
    - "kernels for connected dominating set on graphs with excluded topological subgraphs"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "structure theorem and isomorphism test for graphs with excluded topological subgraphs"
    - "homomorphism preservation on quasi wide classes"

Connected through hyperedge HE-6 (induced by source paper "a single exponential fixed parameter algorithm for distance hereditary vertex deletion"):
  with members:
    - "the behavior of clique width under graph operations and graph transformations"
    - "a polynomial kernel for distance hereditary vertex deletion"
    - "a faster fpt algorithm and a smaller kernel for block graph vertex deletion"
    - "solving problems on graphs of high rank width"
    - "meta kernelization using well structured modulators"
    - "a polynomial kernel for block graph deletion"
    - "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion"
    - "linear kernels and single exponential algorithms via protrusion decompositions"

Connected through hyperedge HE-7 (induced by source paper "polynomial kernels and wideness properties of nowhere dense graph classes"):
  with members:
    - "bidimensionality and kernels"
    - "neighborhood complexity and kernelization for nowhere dense classes of graphs"
    - "kernelization and sparseness the case of dominating set"
    - "deciding first order properties of nowhere dense graphs"
    - "kernels for connected dominating set on graphs with excluded topological subgraphs"
    - "structure theorem and isomorphism test for graphs with excluded topological subgraphs"
    - "homomorphism preservation on quasi wide classes"
    - "polynomial time data reduction for dominating set"

Connected through hyperedge HE-8 (induced by source paper "approximation and kernelization for chordal vertex deletion"):
  with members:
    - "uniform kernelization complexity of hitting forbidden minors"
    - "linear recognition of almost interval graphs"
    - "a subexponential parameterized algorithm for proper interval completion"
    - "a polynomial kernel for proper interval vertex deletion"
    - "preprocessing subgraph and minor problems when does a small vertex cover help"
    - "approximation and tidying a problem kernel for s plex cluster vertex deletion"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

Question: Please tell me which class the center node 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)