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: "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.

Connected through hyperedge HE-1 (induced by source paper "trade offs in dynamic coloring for bipartite and general graphs"):
  with members:
    - "improved dynamic graph coloring"
    - "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:
    - "improved dynamic graph coloring"
    - "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 "fully dynamic δ 1 coloring in constant update time"):
  with members:
    - "fully dynamic maximal independent set in expected poly log update time"
    - "fully dynamic maximal independent set with polylogarithmic update time"
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "dynamic algorithms for graph coloring"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "fully dynamic maximal matching in constant update time"
    - "fully dynamic maximal matching in o log n update time"

Connected through hyperedge HE-4 (induced by source paper "on a decentralized delta 1 graph coloring algorithm"):
  with members:
    - "dynamic algorithms for graph coloring"
    - "decentralized constraint satisfaction"
    - "a constructive proof of the general lovasz local lemma"

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)