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 "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing"):
  Abstract: With few exceptions (namely, algorithms for maximal matching, $2$-approximate vertex cover, and certain constant-stretch spanners), all known fully dynamic algorithms in general graphs require (amortized) $\Omega(\log n)$ update/query time. Showing for the first time that techniques from property testing can lead to constant-time fully dynamic graph algorithms we prove the following results: #R##N#(1) We give a fully dynamic (Las-Vegas style) algorithm with constant expected amortized time per update that maintains a proper $(\Delta+1)$-vertex coloring of a graph with maximum degree at most $\Delta$. This improves upon the previous $O(\log \Delta)$-time algorithm by Bhattacharya et al. (SODA 2018). We show that our result does not only have optimal running time, but is also optimal in the sense that already deciding whether a $\Delta$-coloring exists in a dynamically changing graph with maximum degree at most $\Delta$ takes $\Omega(\log n)$ time per operation. #R##N#(2) We give two fully dynamic algorithms that maintain a $(1+\varepsilon)$-approximation of the weight $M$ of the minimum spanning forest of a graph $G$ with edges weights in $[1,W]$. Our deterministic algorithm takes $O({W^2 \log W}/{\varepsilon^3})$ worst-case time, which is constant if both $W$ and $\varepsilon$ are constant. This is somewhat surprising as a lower bound by Patrascu and Demaine (SIAM J. Comput. 2006) shows that it takes $\Omega(\log n)$ time per operation to maintain the exact weight of the MSF that holds even for $W=1$. Our randomized (Monte-Carlo style) algorithm works with high probability and runs in worst-case $O(\frac{1}{\varepsilon^4}\log^2(\frac{1}{\varepsilon}))$ time if $W= O({(m^*)^{1/3}}/{\log^3 n})$, where $m^*$ is the minimum number of edges in the graph throughout all the updates. It works even against an adaptive adversary.

Includes members:
  Member "testable properties in general graphs and random order streaming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a characterization of graph properties testable for general planar graphs with one sided error it is all about forbidden subgraphs")
  Member "improved dynamic graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trade offs in dynamic coloring for bipartite and general graphs")
  Member "estimating graph parameters from random order streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "testable bounded degree graph properties are random order streamable":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "dynamic algorithms for graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "dynamic graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "fully dynamic maximal matching in constant 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)