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 "structurally parameterized d scattered set"):
  Abstract: In $d$-Scattered Set we are given an (edge-weighted) graph and are asked to select at least $k$ vertices, so that the distance between any pair is at least $d$, thus generalizing Independent Set. We provide upper and lower bounds on the complexity of this problem with respect to various standard graph parameters. In particular, we show the following: #R##N#- For any $d\ge2$, an $O^*(d^{\textrm{tw}})$-time algorithm, where $\textrm{tw}$ is the treewidth of the input graph. #R##N#- A tight SETH-based lower bound matching this algorithm's performance. These generalize known results for Independent Set. #R##N#- $d$-Scattered Set is W[1]-hard parameterized by vertex cover (for edge-weighted graphs), or feedback vertex set (for unweighted graphs), even if $k$ is an additional parameter. #R##N#- A single-exponential algorithm parameterized by vertex cover for unweighted graphs, complementing the above-mentioned hardness. #R##N#- A $2^{O(\textrm{td}^2)}$-time algorithm parameterized by tree-depth ($\textrm{td}$), as well as a matching ETH-based lower bound, both for unweighted graphs. #R##N#We complement these mostly negative results by providing an FPT approximation scheme parameterized by treewidth. In particular, we give an algorithm which, for any error parameter $\epsilon > 0$, runs in time $O^*((\textrm{tw}/\epsilon)^{O(\textrm{tw})})$ and returns a $d/(1+\epsilon)$-scattered set of size $k$, if a $d$-scattered set of the same size exists.

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 "structural parameters tight bounds and approximation for k r center":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regular intersection emptiness of graph problems finding a needle in a haystack of graphs with the help of automata")
  Member "on distance d independent set and other problems in graphs with few minimal separators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "potential maximal cliques parameterized by edge clique cover")
  Member "optimal parameterized algorithms for planar facility location problems using voronoi diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grundy coloring friends half graphs bicliques")
  Member "optimal dynamic program for r domination problems over tree decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "new algorithms for mixed dominating set")
  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)