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: "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.

Connected through hyperedge HE-1 (induced by source paper "new algorithms for mixed dominating set"):
  with members:
    - "parameterized orientable deletion"
    - "tight approximation ratio for minimum maximal matching"
    - "finer tight bounds for coloring on clique width"
    - "structural parameters tight bounds and approximation for k r center"
    - "an efficient algorithm for mixed domination on generalized series parallel graphs"
    - "fine grained parameterized complexity analysis of graph coloring problems"
    - "on fixed parameter tractability of the mixed domination problem for graphs with bounded tree width"
    - "optimal dynamic program for r domination problems over tree decompositions"

Connected through hyperedge HE-2 (induced by source paper "improved in approximability bounds for d scattered set"):
  with members:
    - "kernelization and approximation of distance r independent sets on nowhere dense graphs"
    - "on distance d independent set and other problems in graphs with few minimal separators"
    - "time approximation trade offs for inapproximable problems"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "independent set induced matching and pricing connections and tight subexponential time approximation hardnesses"
    - "bidimensionality and eptas"
    - "exponential time approximation of hard problems"

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.CC(Computational Complexity)