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: "polynomial kernels for hitting forbidden minors under structural parameterizations"
  Abstract: We investigate polynomial-time preprocessing for the problem of hitting forbidden minors in a graph, using the framework of kernelization. For a fixed finite set of connected graphs F, the F-Deletion problem is the following: given a graph G and integer k, is it possible to delete k vertices from G to ensure the resulting graph does not contain any graph from F as a minor? Earlier work by Fomin, Lokshtanov, Misra, and Saurabh [FOCS'12] showed that when F contains a planar graph, an instance (G,k) can be reduced in polynomial time to an equivalent one of size $k^{O(1)}$. In this work we focus on structural measures of the complexity of an instance, with the aim of giving nontrivial preprocessing guarantees for instances whose solutions are large. Motivated by several impossibility results, we parameterize the F-Deletion problem by the size of a vertex modulator whose removal results in a graph of constant treedepth $\eta$. #R##N#We prove that for each set F of connected graphs and constant $\eta$, the F-Deletion problem parameterized by the size of a treedepth-$\eta$ modulator has a polynomial kernel. Our kernelization is fully explicit and does not depend on protrusion reduction or well-quasi-ordering, which are sources of algorithmic non-constructivity in earlier works on F-Deletion. Our main technical contribution is to analyze how models of a forbidden minor in a graph G with modulator X, interact with the various connected components of G-X. By bounding the number of different types of behavior that can occur by a polynomial in |X|, we obtain a polynomial kernel using a recursive preprocessing strategy. Our results extend earlier work for specific instances of F-Deletion such as Vertex Cover and Feedback Vertex Set. It also generalizes earlier preprocessing results for F-Deletion parameterized by a vertex cover, which is a treedepth-one modulator.

Connected through hyperedge HE-1 (induced by source paper "elimination distances blocking sets and kernels for vertex cover"):
  with members:
    - "smaller parameters for vertex cover kernelization"
    - "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-2 (induced by source paper "a turing kernelization dichotomy for structural parameterizations of mathcal f minor free deletion"):
  with members:
    - "turing kernelization for finding long paths and cycles in restricted graph classes"
    - "linear time kernelization for feedback vertex set"
    - "uniform kernelization complexity of hitting forbidden minors"
    - "data reduction for graph coloring problems"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"
    - "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.CC(Computational Complexity)