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 "faster approximation algorithms for computing shortest cycles on weighted graphs"):
  Abstract: Given an $n$-vertex $m$-edge graph $G$ with non negative edge-weights, the girth of $G$ is the weight of a shortest cycle in $G$. For any graph $G$ with polynomially bounded integer weights, we present a deterministic algorithm that computes, in $\tilde{\cal O}(n^{5/3}+m)$-time, a cycle of weight at most twice the girth of $G$. Our approach combines some new insights on the previous approximation algorithms for this problem (Lingas and Lundell, IPL'09; Roditty and Tov, TALG'13) with Hitting Set based methods that are used for approximate distance oracles and date back from (Thorup and Zwick, JACM'05). Then, we turn our algorithm into a deterministic $(2+\varepsilon)$-approximation for graphs with arbitrary non negative edge-weights, at the price of a slightly worse running-time in $\tilde{\cal O}(n^{5/3}\log^{{\cal O}(1)}{(1/\varepsilon)}+m)$. Finally, if we insist in removing the dependency in the number $m$ of edges, we can transform our algorithms into an $\tilde{\cal O}(n^{5/3})$-time randomized $4$-approximation for the graphs with non negative edge-weights -- assuming the adjacency lists are sorted. Combined with the aforementioned Hitting Set based methods, this algorithm can be derandomized, thereby yielding an $\tilde{\cal O}(n^{5/3})$-time deterministic $4$-approximation for the graphs with polynomially bounded integer weights, and an $\tilde{\cal O}(n^{5/3}\log^{{\cal O}(1)}{(1/\varepsilon)})$-time deterministic $(4+\varepsilon)$-approximation for the graphs with non negative edge-weights. To the best of our knowledge, these are the first known subquadratic-time approximation algorithms for computing the girth of weighted graphs.

Includes members:
  Member "towards tight approximation bounds for graph diameter and eccentricities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph spanners a tutorial review")
  Member "fully polynomial fpt algorithms for some classes of bounded clique width graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "iterated type partitions")
  Member "new subquadratic approximation algorithms for the girth":
    also connected through other hyperedges:
      (none)
  Member "tight hardness for shortest cycles and paths in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "on the power of tree depth for fully polynomial fpt algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on adaptive algorithms for maximum matching")
  Member "approximating cycles in directed graphs fast algorithms for girth and roundtrip spanners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved girth approximation and roundtrip spanners")
  Member "subtree isomorphism revisited":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tighter connections between formula sat and shaving logs")
  Member "polynomial fixed parameter algorithms a case study for longest path on interval graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing maximum matchings in temporal graphs")

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)