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: "approximation and tidying a problem kernel for s plex cluster vertex deletion"
  Abstract: We introduce the NP-hard graph-based data clustering problem s-Plex Cluster Vertex Deletion, where the task is to delete at most k vertices from a graph so that the connected components of the resulting graph are s-plexes. In an s-plex, every vertex has an edge to all but at most s−1 other vertices; cliques are 1-plexes. We propose a new method based on “approximation and tidying” for kernelizing vertex deletion problems whose goal graphs can be characterized by forbidden induced subgraphs. The method exploits polynomial-time approximation results and thus provides a useful link between approximation and kernelization. Employing “approximation and tidying”, we develop data reduction rules that, in O(ksn 2) time, transform an s-Plex Cluster Vertex Deletion instance with n vertices into an equivalent instance with O(k 2 s 3) vertices, yielding a problem kernel. To this end, we also show how to exploit structural properties of the specific problem in order to significantly improve the running time of the proposed kernelization method.

Connected through hyperedge HE-1 (induced by source paper "on adaptive algorithms for maximum matching"):
  with members:
    - "a quasi linear time b matching algorithm on distance hereditary graphs and bounded split width graphs"
    - "efficient and adaptive parameterized algorithms on modular decompositions"
    - "parameterized aspects of triangle enumeration"
    - "robust and adaptive search"
    - "on the power of tree depth for fully polynomial fpt algorithms"
    - "simplified algorithmic metatheorems beyond mso treewidth and neighborhood diversity"
    - "a linear time algorithm for maximum cardinality matching on cocomparability graphs"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

Connected through hyperedge HE-2 (induced by source paper "clustering to given connectivities"):
  with members:
    - "reducing cmso model checking to highly connected graphs"
    - "parameterized algorithms for partitioning graphs into highly connected clusters"
    - "clustering with local restrictions"
    - "finding connected secluded subgraphs"
    - "spanning circuits in regular matroids"
    - "fully polynomial time parameterized computations for graphs and matrices of low treewidth"
    - "parameterized algorithms for min max multiway cut and list digraph homomorphism"
    - "a short survey on data clustering algorithms"

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"
    - "a shortcut to sun flowers kernels in logarithmic space or linear time"
    - "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"
    - "finding small separators in linear time via treewidth reduction"

Connected through hyperedge HE-4 (induced by source paper "finding secluded places of special interest in graphs"):
  with members:
    - "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"
    - "finding small separators in linear time via treewidth reduction"

Connected through hyperedge HE-5 (induced by source paper "clustering with local restrictions"):
  with members:
    - "fixed parameter tractability of multicut parameterized by the size of the cutset"
    - "online correlation clustering"
    - "almost 2 sat is fixed parameter tractable"

Connected through hyperedge HE-6 (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"
    - "kernelization using structural parameters on sparse graph classes"
    - "a polynomial kernel for proper interval vertex deletion"
    - "preprocessing subgraph and minor problems when does a small vertex cover help"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

Connected through hyperedge HE-7 (induced by source paper "parameterized algorithmics for graph modification problems on interactions with heuristics"):
  with members:
    - "win win kernelization for degree sequence completion problems"
    - "improved upper and lower bound heuristics for degree anonymization in social networks"
    - "parameterized algorithmics for computational social choice nine research challenges"
    - "faster parameterized algorithms using linear programming"

Connected through hyperedge HE-8 (induced by source paper "towards optimal and expressive kernelization for d hitting set"):
  with members:
    - "a polynomial kernel for proper interval vertex deletion"

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.DM(Discrete Mathematics)