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: "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.

Connected through hyperedge HE-1 (induced by source paper "trade offs in dynamic coloring for bipartite and general graphs"):
  with members:
    - "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing"
    - "dynamic coloring for bipartite and general graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "fully dynamic maximal matching in constant update time"
    - "simple deterministic algorithms for fully dynamic maximal matching"
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-2 (induced by source paper "dynamic data structures for interval coloring"):
  with members:
    - "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing"
    - "dynamic algorithms for graph coloring"
    - "faster online matrix vector multiplication"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"

Connected through hyperedge HE-3 (induced by source paper "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing"):
  with members:
    - "testable properties in general graphs and random order streaming"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"
    - "optimal dynamic distributed mis"

Connected through hyperedge HE-4 (induced by source paper "fully dynamic data structures for interval coloring"):
  with members:
    - "dynamic graph coloring"

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.DS(Data Structures and Algorithms)