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: "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.

Connected through hyperedge HE-1 (induced by source paper "measuring what matters a hybrid approach to dynamic programming with treewidth"):
  with members:
    - "finding hamiltonian cycle in graphs of bounded treewidth experimental evaluation"
    - "slightly superexponential parameterized problems"
    - "a unified polynomial time algorithm for feedback vertex set on graphs of bounded mim width"
    - "a polynomial kernel for distance hereditary vertex deletion"
    - "combining treewidth and backdoors for csp"
    - "a single exponential fixed parameter algorithm for distance hereditary vertex deletion"
    - "maximum matching width new characterizations and a fast algorithm for dominating set"
    - "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion"

Connected through hyperedge HE-2 (induced by source paper "backdoors into heterogeneous classes of sat and csp"):
  with members:
    - "combining treewidth and backdoors for csp"
    - "discovering archipelagos of tractability for constraint satisfaction and counting"
    - "solving problems on graphs of high rank width"
    - "on backdoors to tractable constraint languages"
    - "backdoors to abduction"
    - "strong backdoors to bounded treewidth sat"
    - "the tractability of csp classes defined by forbidden patterns"
    - "backdoors to satisfaction"

Connected through hyperedge HE-3 (induced by source paper "how much does a treedepth modulator help to obtain polynomial kernels beyond sparse graphs"):
  with members:
    - "bidimensionality and kernels"
    - "vertex cover structural parameterization revisited"
    - "a faster parameterized algorithm for treedepth"
    - "explicit linear kernels via dynamic programming"
    - "meta kernelization with structural parameters"
    - "kernelization lower bounds by cross composition"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "dynamic programming for graphs on surfaces"

Connected through hyperedge HE-4 (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"
    - "a polynomial kernel for block graph deletion"
    - "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion"
    - "kernelization using structural parameters on sparse graph classes"
    - "linear kernels and single exponential algorithms via protrusion decompositions"

Connected through hyperedge HE-5 (induced by source paper "solving problems on graphs of high rank width"):
  with members:
    - "on feedback vertex set new measure and new structures"
    - "chordal editing is fixed parameter tractable"
    - "kernelization using structural parameters on sparse graph classes"
    - "meta kernelization with structural parameters"
    - "backdoors to satisfaction"
    - "practical and efficient split decomposition via graph labelled trees"
    - "kernel bounds for path and cycle problems"
    - "linear time split decomposition revisited"

Connected through hyperedge HE-6 (induced by source paper "meta kernelization with structural parameters"):
  with members:
    - "guarantees and limits of preprocessing in constraint satisfaction and reasoning"
    - "kernelization using structural parameters on sparse graph classes"
    - "preprocessing for treewidth a combinatorial analysis through kernelization"
    - "linear time split decomposition revisited"

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)