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 connectivity decomposition"
  Abstract: We present time-efficient distributed algorithms for decomposing graphs with large edge or vertex connectivity into multiple spanning or dominating trees, respectively. As their primary applications, these decompositions allow us to achieve information flow with size close to the connectivity by parallelizing it along the trees. More specifically, our distributed decomposition algorithms are as follows: #R##N#(I) A decomposition of each undirected graph with vertex-connectivity $k$ into (fractionally) vertex-disjoint weighted dominating trees with total weight $\Omega(\frac{k}{\log n})$, in $\widetilde{O}(D+\sqrt{n})$ rounds. #R##N#(II) A decomposition of each undirected graph with edge-connectivity $\lambda$ into (fractionally) edge-disjoint weighted spanning trees with total weight $\lceil\frac{\lambda-1}{2}\rceil(1-\varepsilon)$, in $\widetilde{O}(D+\sqrt{n\lambda})$ rounds. #R##N#We also show round complexity lower bounds of $\tilde{\Omega}(D+\sqrt{\frac{n}{k}})$ and $\tilde{\Omega}(D+\sqrt{\frac{n}{\lambda}})$ for the above two decompositions, using techniques of [Das Sarma et al., STOC'11]. Moreover, our vertex-connectivity decomposition extends to centralized algorithms and improves the time complexity of [Censor-Hillel et al., SODA'14] from $O(n^3)$ to near-optimal $\tilde{O}(m)$. #R##N#As corollaries, we also get distributed oblivious routing broadcast with $O(1)$-competitive edge-congestion and $O(\log n)$-competitive vertex-congestion. Furthermore, the vertex connectivity decomposition leads to near-time-optimal $O(\log n)$-approximation of vertex connectivity: centralized $\widetilde{O}(m)$ and distributed $\tilde{O}(D+\sqrt{n})$. The former moves toward the 1974 conjecture of Aho, Hopcroft, and Ullman postulating an $O(m)$ centralized exact algorithm while the latter is the first distributed vertex connectivity approximation.

Connected through hyperedge HE-1 (induced by source paper "distributed edge connectivity in sublinear time"):
  with members:
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "expander decomposition and pruning faster stronger and simpler"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"

Connected through hyperedge HE-2 (induced by source paper "computing and testing small connectivity in near linear time and queries via fast local cut algorithms"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"
    - "on sampling edges almost uniformly"
    - "local flow partitioning for faster edge connectivity"

Connected through hyperedge HE-3 (induced by source paper "breaking quadratic time for small vertex connectivity and an approximation scheme"):
  with members:
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "expander decomposition and pruning faster stronger and simpler"
    - "capacity releasing diffusion for speed and locality"
    - "local flow partitioning for faster edge connectivity"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "a simple and strongly local flow based method for cut improvement"
    - "fully dynamic minimum spanning forest with improved worst case update time"
    - "deterministic global minimum cut of a simple graph in near linear time"

Connected through hyperedge HE-4 (induced by source paper "computing and testing small vertex connectivity in near linear time and queries"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"

Connected through hyperedge HE-5 (induced by source paper "distributed exact weighted all pairs shortest paths in near linear time"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "new and simplified distributed algorithms for weighted all pairs shortest paths"
    - "a deterministic distributed algorithm for weighted all pairs shortest paths through pipelining"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"
    - "distributed exact weighted all pairs shortest paths in tilde o n 5 4 rounds"
    - "improved distributed algorithms for exact shortest paths"

Connected through hyperedge HE-6 (induced by source paper "distributed approximation of minimum k edge connected spanning subgraphs"):
  with members:
    - "distributed spanner approximation"
    - "fast distributed approximation for tap and 2 edge connectivity"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "almost tight distributed minimum cut algorithms"
    - "distributed minimum cut approximation"
    - "distributed verification and hardness of distributed approximation"
    - "local computation lower and upper bounds"

Connected through hyperedge HE-7 (induced by source paper "connectivity in interdependent networks"):
  with members:
    - "maximizing reliability in wdm networks through lightpath routing"
    - "a new perspective on vertex connectivity"

Connected through hyperedge HE-8 (induced by source paper "fast distributed approximation for tap and 2 edge connectivity"):
  with members:
    - "improved distributed approximations for minimum weight two edge connected spanning subgraph"
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "distributed approximation of minimum k edge connected spanning subgraphs"
    - "improved approximation for tree augmentation saving by rewiring"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "a simplified 1 5 approximation algorithm for augmenting edge connectivity of a graph from 1 to 2"
    - "approximating unweighted tree augmentation via lift and project part ii"

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)