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: "a shortcut to sun flowers kernels in logarithmic space or linear time"
  Abstract: We investigate whether kernelization results can be obtained if we restrict kernelization algorithms to run in logarithmic space. This restriction for kernelization is motivated by the question of what results are attainable for preprocessing via simple and/or local reduction rules. We find kernelizations for d-Hitting Set(k), d-Set Packing(k), Edge Dominating Set(k) and a number of hitting and packing problems in graphs, each running in logspace. Additionally, we return to the question of linear-time kernelization. For d-Hitting Set(k) a linear-time kernelization was given by van Bevern [Algorithmica (2014)]. We give a simpler procedure and save a large constant factor in the size bound. Furthermore, we show that we can obtain a linear-time kernel for d-Set Packing(k) as well.

Connected through hyperedge HE-1 (induced by source paper "optimal size problem kernels for d hitting set in linear time and space"):
  with members:
    - "understanding the effectiveness of data reduction in public transportation networks"
    - "on approximate data reduction for the rural postman problem theory and experiments"
    - "computing hitting set kernels by ac 0 circuits"
    - "efficiently enumerating hitting sets of hypergraphs arising in data profiling"
    - "exploiting hidden structure in selecting dimensions that distinguish vectors"
    - "towards optimal and expressive kernelization for d hitting set"

Connected through hyperedge HE-2 (induced by source paper "faster fpt algorithm for 5 path vertex cover"):
  with members:
    - "an o 2 619 k algorithm for 4 path vertex cover"
    - "parameterized algorithm for 3 path vertex cover"
    - "exact algorithms via monotone local search"

Connected through hyperedge HE-3 (induced by source paper "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"):
  with members:
    - "finding secluded places of special interest in graphs"
    - "parameterized complexity of secluded connectivity problems"
    - "towards optimal and expressive kernelization for d hitting set"
    - "on the parameterized complexity of cutting a few vertices from a graph"
    - "secluded connectivity problems"
    - "approximation and tidying a problem kernel for s plex cluster vertex deletion"
    - "finding small separators in linear time via treewidth reduction"

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)