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: "distributed domination on graph classes of bounded expansion"
  Abstract: We provide a new constant factor approximation algorithm for the (connected) distance-$r$ dominating set problem on graph classes of bounded expansion. Classes of bounded expansion include many familiar classes of sparse graphs such as planar graphs and graphs with excluded (topological) minors, and notably, these classes form the most general subgraph closed classes of graphs for which a sequential constant factor approximation algorithm for the distance-$r$ dominating set problem is currently known. Our algorithm can be implemented in the \congestbc model of distributed computing and uses $\mathcal{O}(r^2 \log n)$ communication rounds. #R##N#Our techniques, which may be of independent interest, are based on a distributed computation of sparse neighborhood covers of small radius on bounded expansion classes. We show how to compute an $r$-neighborhood cover of radius~$2r$ and overlap $f(r)$ on every class of bounded expansion in $\mathcal{O}(r^2 \log n)$ communication rounds for some function~$f$.% in the $\mathcal{CONGEST}_{\mathrm{BC}}$ model. #R##N#Finally, we show how to use the greater power of the $\mathcal{LOCAL}$ model to turn any distance-$r$ dominating set into a constantly larger connected distance-$r$ dominating set in $3r+1$ rounds on any class of bounded expansion. Combining this algorithm, e.g., with the constant factor approximation algorithm for dominating sets on planar graphs of Lenzen et al.\ gives a constant factor approximation algorithm for connected dominating sets on planar graphs in a constant number of rounds in the $\mathcal{LOCAL}$ model, where the approximation ratio is only $6$ times larger than that of Lenzen et al.'s algorithm.

Connected through hyperedge HE-1 (induced by source paper "nowhere dense graph classes and algorithmic applications a tutorial at highlights of logic games and automata 2019"):
  with members:
    - "classes of graphs with low complexity the case of classes with bounded linear rankwidth"
    - "kernelization and approximation of distance r independent sets on nowhere dense graphs"
    - "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"
    - "on low rank width colorings"
    - "on the number of types in sparse graphs"
    - "the generalised colouring numbers on classes of bounded expansion"
    - "polynomial kernels and wideness properties of nowhere dense graph classes"
    - "deciding first order properties of nowhere dense graphs"

Connected through hyperedge HE-2 (induced by source paper "distributed distance r dominating set on sparse high girth graphs"):
  with members:
    - "deterministic distributed dominating set approximation in the congest model"
    - "local computation lower and upper bounds"

Connected through hyperedge HE-3 (induced by source paper "empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi wideness"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of kernelization algorithms to dominating set"
    - "polynomial bounds for centered colorings on proper minor closed graph classes"
    - "kernelization and approximation of distance r independent sets on nowhere dense graphs"
    - "reconfiguration on nowhere dense graph classes"
    - "positive instance driven dynamic programming for treewidth"
    - "experimental evaluation of counting subgraph isomorphisms in classes of bounded expansion"
    - "on the number of types in sparse graphs"

Connected through hyperedge HE-4 (induced by source paper "parameterized circuit complexity of model checking first order logic on sparse structures"):
  with members:
    - "polynomial kernels and wideness properties of nowhere dense graph classes"
    - "neighborhood complexity and kernelization for nowhere dense classes of graphs"
    - "kernelization and sparseness the case of dominating set"

Connected through hyperedge HE-5 (induced by source paper "distributed dominating set approximations beyond planar graphs"):
  with members:
    - "greedy domination on biclique free graphs"
    - "approximation algorithms for polynomial expansion and low density graphs"
    - "a fast network decomposition algorithm and its applications to constant time distributed computation"
    - "analytical approach to parallel repetition"
    - "local computation lower and upper bounds"

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.DC(Distributed, Parallel, and Cluster Computing)