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 "structural parameters tight bounds and approximation for k r center"):
  Abstract: In $(k,r)$-Center we are given a (possibly edge-weighted) graph and are asked to select at most $k$ vertices (centers), so that all other vertices are at distance at most $r$ from a center. In this paper we provide a number of tight fine-grained bounds on the complexity of this problem with respect to various standard graph parameters. Specifically: #R##N#- For any $r\ge 1$, we show an algorithm that solves the problem in $O^*((3r+1)^{\textrm{cw}})$ time, where $\textrm{cw}$ is the clique-width of the input graph, as well as a tight SETH lower bound matching this algorithm's performance. As a corollary, for $r=1$, this closes the gap that previously existed on the complexity of Dominating Set parameterized by $\textrm{cw}$. #R##N#- We strengthen previously known FPT lower bounds, by showing that $(k,r)$-Center is W[1]-hard parameterized by the input graph's vertex cover (if edge weights are allowed), or feedback vertex set, even if $k$ is an additional parameter. Our reductions imply tight ETH-based lower bounds. Finally, we devise an algorithm parameterized by vertex cover for unweighted graphs. #R##N#- We show that the complexity of the problem parameterized by tree-depth is $2^{\Theta(\textrm{td}^2)}$ by showing an algorithm of this complexity and a tight ETH-based lower bound. #R##N#We complement these mostly negative results by providing FPT approximation schemes parameterized by clique-width or treewidth which work efficiently independently of the values of $k,r$. In particular, we give algorithms which, for any $\epsilon>0$, run in time $O^*((\textrm{tw}/\epsilon)^{O(\textrm{tw})})$, $O^*((\textrm{cw}/\epsilon)^{O(\textrm{cw})})$ and return a $(k,(1+\epsilon)r)$-center, if a $(k,r)$-center exists, thus circumventing the problem's W-hardness.

Includes members:
  Member "parameterized power vertex cover":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polynomial time data reduction for weighted problems beyond additive goal functions")
  Member "parameterized approximation algorithms for some location problems in graphs":
    also connected through other hyperedges:
      (none)
  Member "fixed parameter approximations for k center problems in low highway dimension graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "travelling on graphs with small highway dimension")
  Member "optimal dynamic program for r domination problems over tree decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new algorithms for mixed dominating set")
  Member "faster algorithms for vertex partitioning problems parameterized by clique width":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "node multiway cut and subset feedback vertex set on graphs of bounded mim width")
  Member "parameterized edge hamiltonicity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on computing the hamiltonian index of graphs")
  Member "parameterized approximation schemes using graph widths":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parameterized approximation schemes for independent set of rectangles and geometric knapsack")
  Member "known algorithms on graphs of bounded treewidth are probably optimal":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the complexity of finding large odd induced subgraphs and odd colorings")

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.CC(Computational Complexity)