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 distributed approximations for minimum weight two edge connected spanning subgraph"):
  Abstract: The minimum-weight $2$-edge-connected spanning subgraph (2-ECSS) problem is a natural generalization of the well-studied minimum-weight spanning tree (MST) problem, and it has received considerable attention in the area of network design. The latter problem asks for a minimum-weight subgraph with an edge connectivity of $1$ between each pair of vertices while the former strengthens this edge-connectivity requirement to $2$. Despite this resemblance, the 2-ECSS problem is considerably more complex than MST. While MST admits a linear-time centralized exact algorithm, 2-ECSS is NP-hard and the best known centralized approximation algorithm for it (that runs in polynomial time) gives a $2$-approximation. #R##N#In this paper, we give a deterministic distributed algorithm with round complexity of $\widetilde{O}(D+\sqrt{n})$ that computes a $(5+\epsilon)$-approximation of 2-ECSS, for any constant $\epsilon>0$. Up to logarithmic factors, this complexity matches the $\widetilde{\Omega}(D+\sqrt{n})$ lower bound that can be derived from Das Sarma et al. [STOC'11], as shown by Censor-Hillel and Dory [OPODIS'17]. Our result is the first distributed constant approximation for 2-ECSS in the nearly optimal time and it improves on a recent randomized algorithm of Dory [PODC'18], which achieved an $O(\log n)$-approximation in $\widetilde{O}(D+\sqrt{n})$ rounds. #R##N#We also present an alternative algorithm for $O(\log n)$-approximation, whose round complexity is linear in the low-congestion shortcut parameter of the network, following a framework introduced by Ghaffari and Haeupler [SODA'16]. This algorithm has round complexity $\widetilde{O}(D+\sqrt{n})$ in worst-case networks but it provably runs much faster in many well-behaved graph families of interest. For instance, it runs in $\widetilde{O}(D)$ time in planar networks and those with bounded genus, bounded path-width or bounded tree-width.

Includes members:
  Member "distributed edge connectivity in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "small cuts and connectivity certificates a fault tolerant approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "minor excluded network families admit fast distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "near optimal schedules for simultaneous multicasts")
  Member "new distributed algorithms in almost mixing time via transformations from parallel algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal schedules for simultaneous multicasts")
  Member "distributed approximation of minimum k edge connected spanning subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "fast distributed approximation for tap and 2 edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hardness of distributed optimization")
  Member "low congestion shortcuts without embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "near optimal schedules for simultaneous multicasts")
  Member "almost tight distributed minimum cut algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved algorithm for min cuts in distributed networks")

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)