human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "uniform kernelization complexity of hitting forbidden minors"):
  Abstract: The F-Minor-Free Deletion problem asks, for a fixed set F and an input consisting of a graph G and integer k, whether k vertices can be removed from G such that the resulting graph does not contain any member of F as a minor. This paper analyzes to what extent provably effective and efficient preprocessing is possible for F-Minor-Free Deletion. Fomin et al. (FOCS 2012) showed that the special case Planar F-Deletion (when F contains at least one planar graph) has a kernel of size f(F) * k^{g(F)} for some functions f and g. The degree g of the polynomial grows very quickly; it is not even known to be computable. Fomin et al. left open whether Planar F-Deletion has kernels whose size is uniformly polynomial, i.e., of the form f(F) * k^c for some universal constant c that does not depend on F. Our results in this paper are twofold. (1) We prove that some Planar F-Deletion problems do not have uniformly polynomial kernels (unless NP is in coNP/poly). In particular, we prove that Treewidth-Eta Deletion does not have a kernel with O(k^{eta/4} - eps) vertices for any eps > 0, unless NP is in coNP/poly. In fact, we even prove the kernelization lower bound for the larger parameter vertex cover number. This resolves an open problem of Cygan et al. (IPEC 2011). It is a natural question whether further restrictions on F lead to uniformly polynomial kernels. However, we prove that even when F contains a path, the degree of the polynomial must, in general, depend on the set F. (2) A canonical F-Minor-Free Deletion problem when F contains a path is Treedepth-eta Deletion: can k vertices be removed to obtain a graph of treedepth at most eta? We prove that Treedepth-eta Deletion admits uniformly polynomial kernels with O(k^6) vertices for every fixed eta. In order to develop the kernelization we prove several new results about the structure of optimal treedepth-decompositions.

Includes members:
  Member "turing kernelization for finding long paths and cycles in restricted graph classes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a turing kernelization dichotomy for structural parameterizations of mathcal f minor free deletion")
  Member "bidimensionality and kernels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hitting topological minors is fpt")
  Member "a faster parameterized algorithm for treedepth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hamiltonian cycle parameterized by treedepth in single exponential time and polynomial space")
  Member "polynomial bounds for the grid minor theorem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hitting topological minors is fpt")
  Member "kernelization using structural parameters on sparse graph classes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the parameterized complexity of reconfiguration of connected dominating sets")
  Member "network sparsification for steiner problems on planar and bounded genus graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a relaxation of the directed disjoint paths problem a global congestion metric helps")
  Member "kernel bounds for structural parameterizations of pathwidth":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on sparsification for computing treewidth")
  Member "linear kernels and single exponential algorithms via protrusion decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hitting topological minors is fpt")

Question: Please tell me which class the source paper 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)