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 "distributed spanner approximation"):
  Abstract: We address the fundamental network design problem of constructing approximate minimum spanners. Our contributions are for the distributed setting, providing both algorithmic and hardness results. #R##N#Our main hardness result shows that an $\alpha$-approximation for the minimum directed $k$-spanner problem for $k \geq 5$ requires $\Omega(n /\sqrt{\alpha}\log{n})$ rounds using deterministic algorithms or $\Omega(\sqrt{n }/\sqrt{\alpha}\log{n})$ rounds using randomized ones, in the CONGEST model of distributed computing. Combined with the constant-round $O(n^{\epsilon})$-approximation algorithm in the LOCAL model of [Barenboim, Elkin and Gavoille, 2016], as well as a polylog-round $(1+\epsilon)$-approximation algorithm in the LOCAL model that we show here, our lower bounds for the CONGEST model imply a strict separation between the LOCAL and CONGEST models. Notably, to the best of our knowledge, this is the first separation between these models for a local approximation problem. #R##N#Similarly, a separation between the directed and undirected cases is implied. We also prove a nearly-linear lower bound for the minimum weighted $k$-spanner problem for $k \geq 4$, and we show lower bounds for the weighted 2-spanner problem. #R##N#On the algorithmic side, apart from the aforementioned $(1+\epsilon)$-approximation algorithm for minimum $k$-spanners, our main contribution is a new distributed construction of minimum 2-spanners that uses only polynomial local computations. Our algorithm has a guaranteed approximation ratio of $O(\log(m/n))$ for a graph with $n$ vertices and $m$ edges, which matches the best known ratio for polynomial time sequential algorithms [Kortsarz and Peleg, 1994], and is tight if we restrict ourselves to polynomial local computations. Our approach allows us to extend our algorithm to work also for the directed, weighted, and client-server variants of the problem.

Includes members:
  Member "distributed approximation of minimum k edge connected spanning subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "improved deterministic distributed construction of spanners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near additive spanners in low polynomial deterministic congest time")
  Member "distributed property testing for subgraph freeness revisited":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed detection of cliques in dynamic networks")
  Member "quadratic and near quadratic lower bounds for the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "on derandomizing local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "distributed distance bounded network design through distributed convex programming":
    also connected through other hyperedges:
      (none)
  Member "near linear lower bounds for distributed distance computations even in sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "efficient algorithms for constructing very sparse spanners and emulators":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exponentially faster shortest paths in the congested clique")

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)