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: "smaller parameters for vertex cover kernelization"
  Abstract: We revisit the topic of polynomial kernels for Vertex Cover relative to structural parameters. Our starting point is a recent paper due to Fomin and Str{\o}mme [WG 2016] who gave a kernel with $\mathcal{O}(|X|^{12})$ vertices when $X$ is a vertex set such that each connected component of $G-X$ contains at most one cycle, i.e., $X$ is a modulator to a pseudoforest. We strongly generalize this result by using modulators to $d$-quasi-forests, i.e., graphs where each connected component has a feedback vertex set of size at most $d$, and obtain kernels with $\mathcal{O}(|X|^{3d+9})$ vertices. Our result relies on proving that minimal blocking sets in a $d$-quasi-forest have size at most $d+2$. This bound is tight and there is a related lower bound of $\mathcal{O}(|X|^{d+2-\epsilon})$ on the bit size of kernels. #R##N#In fact, we also get bounds for minimal blocking sets of more general graph classes: For $d$-quasi-bipartite graphs, where each connected component can be made bipartite by deleting at most $d$ vertices, we get the same tight bound of $d+2$ vertices. For graphs whose connected components each have a vertex cover of cost at most $d$ more than the best fractional vertex cover, which we call $d$-quasi-integral, we show that minimal blocking sets have size at most $2d+2$, which is also tight. Combined with existing randomized polynomial kernelizations this leads to randomized polynomial kernelizations for modulators to $d$-quasi-bipartite and $d$-quasi-integral graphs. There are lower bounds of $\mathcal{O}(|X|^{d+2-\epsilon})$ and $\mathcal{O}(|X|^{2d+2-\epsilon})$ for the bit size of such kernels.

Connected through hyperedge HE-1 (induced by source paper "on the approximate compressibility of connected vertex cover"):
  with members:
    - "revisiting connected vertex cover fpt algorithms and lossy kernels"
    - "approximation and kernelization for chordal vertex deletion"
    - "how much does a treedepth modulator help to obtain polynomial kernels beyond sparse graphs"
    - "a randomized polynomial kernelization for vertex cover with a smaller parameter"
    - "vertex cover structural parameterization revisited"
    - "kernelization lower bounds by cross composition"
    - "kernelization of packing problems"
    - "representative sets and irrelevant vertices new tools for kernelization"

Connected through hyperedge HE-2 (induced by source paper "elimination distances blocking sets and kernels for vertex cover"):
  with members:
    - "polynomial kernels for hitting forbidden minors under structural parameterizations"
    - "a randomized polynomial kernelization for vertex cover with a smaller parameter"
    - "vertex cover structural parameterization revisited"
    - "raising the bar for vertex cover fixed parameter tractability above a higher guarantee"
    - "graph isomorphism parameterized by elimination distance to bounded degree"
    - "kernelization lower bounds by cross composition"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "vertex cover kernelization revisited upper and lower bounds for a refined parameter"

Connected through hyperedge HE-3 (induced by source paper "fpt algorithms for generalized feedback vertex set problems"):
  with members:
    - "detecting feedback vertex sets of size k in o star 2 7 k time"
    - "improved analysis of highest degree branching for feedback vertex set"
    - "on feedback vertex set new measure and new structures"
    - "directed subset feedback vertex set is fixed parameter tractable"
    - "subset feedback vertex set is fixed parameter tractable"

Connected through hyperedge HE-4 (induced by source paper "optimal data reduction for graph coloring using low degree polynomials"):
  with members:
    - "fine grained parameterized complexity analysis of graph coloring problems"
    - "optimal sparsification for some binary csps using low degree polynomials"
    - "how much does a treedepth modulator help to obtain polynomial kernels beyond sparse graphs"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "point line cover the easy kernel is essentially tight"
    - "data reduction for graph coloring problems"
    - "list h coloring a graph by removing few vertices"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-5 (induced by source paper "what is known about vertex cover kernelization"):
  with members:
    - "vertex cover structural parameterization revisited"
    - "deleting edges to restrict the size of an epidemic a new application for treewidth"
    - "matchings with lower quotas algorithms and complexity"
    - "efficient classification for metric data"
    - "kernelization lower bounds by cross composition"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "vertex cover kernelization revisited upper and lower bounds for a refined parameter"

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)