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 "improved dynamic graph coloring"):
  Abstract: This paper studies the fundamental problem of graph coloring in fully dynamic graphs. Since the problem of computing an optimal coloring, or even approximating it to within $n^{1-\epsilon}$ for any $\epsilon > 0$, is NP-hard in static graphs, there is no hope to achieve any meaningful computational results for general graphs in the dynamic setting. It is therefore only natural to consider the combinatorial aspects of dynamic coloring, or alternatively, study restricted families of graphs. #R##N#Towards understanding the combinatorial aspects of this problem, one may assume a black-box access to a static algorithm for $C$-coloring any subgraph of the dynamic graph, and investigate the trade-off between the number of colors and the number of recolorings per update step. In WADS'17, Barba et al. devised two complementary algorithms: For any $\beta > 0$ the first (respectively, second) maintains an $O(C \beta n^{1/\beta})$ (resp., $O(C \beta)$)-coloring while recoloring $O(\beta)$ (resp., $O(\beta n^{1/\beta})$) vertices per update. Our contribution is two-fold: #R##N#- We devise a new algorithm for general graphs that improves significantly upon the first trade-off in a wide range of parameters: For any $\beta > 0$, we get a $\tilde{O}(\frac{C}{\beta}\log^2 n)$-coloring with $O(\beta)$ recolorings per update, where the $\tilde{O}$ notation supresses polyloglog$(n)$ factors. In particular, for $\beta=O(1)$ we get constant recolorings with polylog$(n)$ colors; this is an exponential improvement over the previous bound. #R##N#- For uniformly sparse graphs, we use low out-degree orientations to strengthen the above result by bounding the update time of the algorithm rather than the number of recolorings. Then, we further improve this result by introducing a new data structure that refines bounded out-degree edge orientations and is of independent interest.

Includes members:
  Member "fully dynamic mis in uniformly sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explicit and implicit dynamic coloring of graphs with bounded arboricity")
  Member "fully dynamic maximal independent set with sublinear update time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "testing bounded arboricity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the arboricity captures the complexity of sampling edges")
  Member "fully dynamic almost maximal matching breaking the polynomial barrier for worst case time bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rounding dynamic matchings against an adaptive adversary")
  Member "the shortest augmenting paths for online matchings on trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reoptimization via gradual transformations")
  Member "dynamic algorithms for graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "dynamic graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trade offs in dynamic coloring for bipartite and general graphs")
  Member "dynamic minimum spanning forest with subpolynomial worst case update time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")

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.DS(Data Structures and Algorithms)