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: "kernelization of packing problems"
  Abstract: Kernelization algorithms are polynomial-time reductions from a problem to itself that guarantee their output to have a size not exceeding some bound. For example, d-Set Matching for integers d ≥ 3 is the problem of finding a matching of size at least k in a given d-uniform hypergraph and has kernels with O(kd) edges. Recently, Bodlaender et al. [ICALP 2008], Fortnow and Santhanam [STOC 2008], Dell and Van Melkebeek [STOC 2010] developed a framework for proving lower bounds on the kernel size for certain problems, under the complexity-theoretic hypothesis that coNP is not contained in NP/poly. Under the same hypothesis, we show lower bounds for the kernelization of d-Set Matching and other packing problems.#R##N##R##N#Our bounds are tight for d-Set Matching: It does not have kernels with O(kd−e) edges for any e > 0 unless the hypothesis fails. By reduction, this transfers to a bound of O(kd−1−e) for the problem of finding k vertex-disjoint cliques of size d in standard graphs. It is natural to ask for tight bounds on the kernel sizes of such graph packing problems. We make first progress in that direction by showing non-trivial kernels with O(k2.5) edges for the problem of finding k vertex-disjoint paths of three edges each. This does not quite match the best lower bound of O(k2−e) that we can prove.#R##N##R##N#Most of our lower bound proofs follow a general scheme that we discover: To exclude kernels of size O(kd−e) for a problem in d-uniform hypergraphs, one should reduce from a carefully chosen d-partite problem that is still NP-hard. As an illustration, we apply this scheme to the vertex cover problem, which allows us to replace the number-theoretical construction by Dell and Van Melkebeek [STOC 2010] with shorter elementary arguments.

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"
    - "smaller parameters for vertex cover kernelization"
    - "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"
    - "representative sets and irrelevant vertices new tools for kernelization"

Connected through hyperedge HE-2 (induced by source paper "on kernelization for edge dominating set under structural parameters"):
  with members:
    - "a randomized polynomial kernelization for vertex cover with a smaller parameter"
    - "raising the bar for vertex cover fixed parameter tractability above a higher guarantee"
    - "polynomial delay algorithm for listing minimal edge dominating sets in graphs"
    - "kernelization lower bounds by cross composition"
    - "faster parameterized algorithms using linear programming"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "on multiway cut parameterized above lower bounds"
    - "vertex cover kernelization revisited upper and lower bounds for a refined parameter"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for the happy coloring problems"):
  with members:
    - "the parameterized complexity of happy colorings"
    - "algorithms and hardness results for happy coloring problems"
    - "on problems as hard as cnf sat"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "exponential time complexity of the permanent and the tutte polynomial"
    - "quasirandom rumor spreading"

Connected through hyperedge HE-4 (induced by source paper "optimal data reduction for graph coloring using low degree polynomials"):
  with members:
    - "smaller parameters for vertex cover kernelization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "best case and worst case sparsifiability of boolean csps"):
  with members:
    - "optimal data reduction for graph coloring using low degree polynomials"
    - "which np hard sat and csp problems admit exponentially improved algorithms"
    - "kernelization of constraint satisfaction problems a study through universal algebra"
    - "optimal sparsification for some binary csps using low degree polynomials"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "point line cover the easy kernel is essentially tight"
    - "on sparsification for computing treewidth"
    - "weak bases of boolean co clones"

Connected through hyperedge HE-6 (induced by source paper "optimal sparsification for some binary csps using low degree polynomials"):
  with members:
    - "optimal data reduction for graph coloring using low degree polynomials"
    - "best case and worst case sparsifiability of boolean csps"
    - "kernelization of constraint satisfaction problems a study through universal algebra"
    - "complexity and approximability of parameterized max csps"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "nonclassical polynomials as a barrier to polynomial lower bounds"
    - "on sparsification for computing treewidth"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-7 (induced by source paper "assessing the computational complexity of multi layer subgraph detection"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "simultaneous feedback vertex set a parameterized perspective"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "on the k anonymization of time varying and multi layer social graphs"
    - "the parameterized complexity of k biclique"
    - "towards optimal and expressive kernelization for d hitting set"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-8 (induced by source paper "frameworks for solving turing kernel lower bound problem and finding natural candidate problems in np intermediate"):
  with members:
    - "turing kernelization for finding long paths and cycles in restricted graph classes"
    - "abusing the tutte matrix an algebraic instance compression for the k set cycle problem"
    - "point line cover the easy kernel is essentially tight"
    - "kernelization lower bounds by cross composition"
    - "non existence of polynomial kernels for the test cover problem"
    - "clique cover and graph separation new incompressibility results"
    - "co nondeterminism in compositions a kernelization lower bound for a ramsey type problem"
    - "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)