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: "a tight lower bound for vertex planarization on graphs of bounded treewidth"
  Abstract: In the Vertex Planarization problem one asks to delete the minimum possible number of vertices from an input graph to obtain a planar graph. The parameterized complexity of this problem, parameterized by the solution size (the number of deleted vertices) has recently attracted significant attention. The state-of-the-art algorithm of Jansen, Lokshtanov, and Saurabh [SODA 2014] runs in time $2^{O(k \log k)} \cdot n$ on $n$-vertex graph with a solution of size $k$. It remains open if one can obtain a single-exponential dependency on $k$ in the running time bound. #R##N#One of the core technical contributions of the work of Jansen, Lokshtanov, and Saurabh is an algorithm that solves a weighted variant of Vertex Planarization in time $2^{O(w \log w)} \cdot n$ on graphs of treewidth $w$. In this short note we prove that the running time of this routine is tight under the Exponential Time Hypothesis, even in unweighted graphs and when parameterizing by treedepth. Consequently, it is unlikely that a potential single-exponential algorithm for Vertex Planarization parameterized by the solution size can be obtained by merely improving upon the aforementioned bounded treewidth subroutine.

Connected through hyperedge HE-1 (induced by source paper "a complexity dichotomy for hitting connected minors on bounded treewidth graphs the chair and the banner draw the boundary"):
  with members:
    - "composing dynamic programming tree decomposition based algorithms"
    - "hitting topological minor models in planar graphs is fixed parameter tractable"
    - "slightly superexponential parameterized problems"
    - "optimal algorithms for hitting topological minors on graphs of bounded treewidth"
    - "deleting vertices to graphs of bounded genus"
    - "generalized feedback vertex set problems on bounded treewidth graphs chordality is the key to single exponential parameterized algorithms"
    - "hitting minors on bounded treewidth graphs"
    - "explicit linear kernels for packing problems"

Connected through hyperedge HE-2 (induced by source paper "optimal algorithms for hitting topological minors on graphs of bounded treewidth"):
  with members:
    - "slightly superexponential parameterized problems"
    - "deleting vertices to graphs of bounded genus"
    - "how much does a treedepth modulator help to obtain polynomial kernels beyond sparse graphs"
    - "bidimensionality and kernels"
    - "hitting and harvesting pumpkins"
    - "explicit linear kernels via dynamic programming"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "solving connectivity problems parameterized by treewidth in single exponential time"

Connected through hyperedge HE-3 (induced by source paper "deleting vertices to graphs of bounded genus"):
  with members:
    - "obtaining a planar graph by 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.DS(Data Structures and Algorithms)