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: "kernel bounds for structural parameterizations of pathwidth"
  Abstract: Assuming the AND-distillation conjecture, the Pathwidth problem of determining whether a given graph G has pathwidth at most k admits no polynomial kernelization with respect to k. The present work studies the existence of polynomial kernels for Pathwidth with respect to other, structural, parameters.#R##N##R##N#Our main result is that, unless NP ⊆ coNP/poly, Pathwidth admits no polynomial kernelization even when parameterized by the vertex deletion distance to a clique, by giving a cross-composition from Cutwidth. The cross-composition works also for Treewidth, improving over previous lower bounds by the present authors. For Pathwidth, our result rules out polynomial kernels with respect to the distance to various classes of polynomial-time solvable inputs, like interval or cluster graphs.#R##N##R##N#This leads to the question whether there are nontrivial structural parameters for which Pathwidth does admit a polynomial kernelization. To answer this, we give a collection of graph reduction rules that are safe for Pathwidth. We analyze the success of these results and obtain polynomial kernelizations with respect to the following parameters: the size of a vertex cover of the graph, the vertex deletion distance to a graph where each connected component is a star, and the vertex deletion distance to a graph where each connected component has at most c vertices.

Connected through hyperedge HE-1 (induced by source paper "uniform kernelization complexity of hitting forbidden minors"):
  with members:
    - "turing kernelization for finding long paths and cycles in restricted graph classes"
    - "bidimensionality and kernels"
    - "a faster parameterized algorithm for treedepth"
    - "polynomial bounds for the grid minor theorem"
    - "kernelization using structural parameters on sparse graph classes"
    - "network sparsification for steiner problems on planar and bounded genus graphs"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "preprocessing subgraph and minor problems when does a small vertex cover help"

Connected through hyperedge HE-2 (induced by source paper "on sparsification for computing treewidth"):
  with members:
    - "tight kernel bounds for problems on graphs with small degeneracy"
    - "fpt is characterized by useful obstruction sets"
    - "kernelization lower bounds by cross composition"
    - "kernelization of packing problems"
    - "preprocessing for treewidth a combinatorial analysis through kernelization"
    - "hitting forbidden minors approximation and kernelization"
    - "cross composition a new technique for kernelization lower bounds"

Connected through hyperedge HE-3 (induced by source paper "kernelization using structural parameters on sparse graph classes"):
  with members:
    - "kernelization and sparseness the case of dominating set"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "kernel bounds for path and cycle problems"
    - "hitting forbidden minors approximation and kernelization"
    - "polynomial time data reduction for dominating set"

Connected through hyperedge HE-4 (induced by source paper "on structural parameterizations for the 2 club problem"):
  with members:
    - "the h index of a graph and its application to dynamic subgraph statistics"
    - "on polynomial kernels for structural parameterizations of odd cycle transversal"

Connected through hyperedge HE-5 (induced by source paper "treewidth and pathwidth parameterized by vertex cover"):
  with members:
    - "preprocessing for treewidth a combinatorial analysis through kernelization"

Connected through hyperedge HE-6 (induced by source paper "preprocessing subgraph and minor problems when does a small vertex cover help"):
  with members:
    - "bidimensionality and kernels"
    - "data reduction for graph coloring problems"
    - "hierarchies of inefficient kernelizability"
    - "kernel bounds for path and cycle problems"
    - "preprocessing for treewidth a combinatorial analysis through kernelization"
    - "on polynomial kernels for structural parameterizations of odd cycle transversal"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"
    - "vertex cover kernelization revisited upper and lower bounds for a refined parameter"

Connected through hyperedge HE-7 (induced by source paper "preprocessing for treewidth a combinatorial analysis through kernelization"):
  with members:
    - "data reduction for graph coloring problems"
    - "kernel bounds for path and cycle problems"
    - "on polynomial kernels for structural parameterizations of odd cycle transversal"
    - "cross composition a new technique for kernelization lower bounds"

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)