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 "efficient construction of directed hopsets and parallel approximate shortest paths"):
  Abstract: The approximate single-source shortest-path problem is as follows: given a graph with nonnegative edge weights and a designated source vertex $s$, return estimates of the distances from~$s$ to each other vertex such that the estimate falls between the true distance and $(1+\epsilon)$ times the distance. This paper provides the first nearly work-efficient parallel algorithm with sublinear span (also called depth) for the approximate shortest-path problem on \emph{directed} graphs. Specifically, for constant $\epsilon$ and polynomially-bounded edge weights, our algorithm has work $\tilde{O}(m)$ and span $n^{1/2+o(1)}$. Several algorithms were previously known for the case of \emph{undirected} graphs, but none of the techniques seem to translate to the directed setting. #R##N#The main technical contribution is the first nearly linear-work algorithm for constructing hopsets on directed graphs. A $(\beta,\epsilon)$-hopset is a set of weighted edges (sometimes called shortcuts) which, when added to the graph, admit $\beta$-hop paths with weight no more than $(1+\epsilon)$ times the true shortest-path distances. There is a simple sequential algorithm that takes as input a directed graph and produces a linear-cardinality hopset with $\beta=O(\sqrt{n})$, but its running time is quite high---specifically $\tilde{O}(m\sqrt{n})$. Our algorithm is the first more efficient algorithm that produces a directed hopset with similar characteristics. Specifically, our sequential algorithm runs in $\tilde{O}(m)$ time and constructs a hopset with $\tilde{O}(n)$ edges and $\beta = n^{1/2+o(1)}$. A parallel version of the algorithm has work $\tilde{O}(m)$ and span $n^{1/2+o(1)}$.

Includes members:
  Member "nearly work efficient parallel algorithm for digraph reachability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster parallel algorithm for approximate shortest path")
  Member "parallel shortest paths using radius stepping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallel approximate undirected shortest paths via low hop emulators")
  Member "hopsets with constant hopbound and applications to approximate shortest paths":
    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 "improved parallel algorithms for spanners and hopsets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient and simple algorithms for fault tolerant spanners")

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)