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 "near optimal sized weight tolerant subgraph for single source shortest path"):
  Abstract: In this paper we address the problem of computing a sparse subgraph of a weighted directed graph such that the exact distances from a designated source vertex to all other vertices are preserved under bounded weight increment. Finding a small sized subgraph that preserves distances between any pair of vertices is a well studied problem. Since in the real world any network is prone to failures, it is natural to study the fault tolerant version of the above problem. Unfortunately, it turns out that there may not always exist such a sparse subgraph even under single edge failure [Demetrescu \emph{et al.} '08]. However in real applications it is not always the case that a link (edge) in a network becomes completely faulty. Instead, it can happen that some links become more congested which can easily be captured by increasing weight on the corresponding edges. Thus it makes sense to try to construct a sparse distance preserving subgraph under the above weight increment model. To the best of our knowledge this problem has not been studied so far. In this paper we show that given any weighted directed graph with $n$ vertices and a source vertex, one can construct a subgraph that contains at most $e \cdot (k-1)!2^kn$ many edges such that it preserves distances between the source and all other vertices as long as the total weight increment is bounded by $k$ and we are allowed to have only integer valued (can be negative) weight on each edge and also weight of an edge can only be increased by some positive integer. Next we show a lower bound of $c\cdot 2^kn$, for some constant $c \ge 5/4$, on the size of the subgraph. We also argue that restriction of integer valued weight and integer valued weight increment are actually essential by showing that if we remove any one of these two restrictions we may need to store $\Omega(n^2)$ edges to preserve distances.

Includes members:
  Member "multiple edge fault tolerant approximate shortest path trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate distance oracles subject to multiple vertex failures")
  Member "an efficient strongly connected components algorithm in the fault tolerant model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a brief note on single source fault tolerant reachability")
  Member "a hierarchy of lower bounds for sublinear additive spanners":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "dual failure resilient bfs structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate distance oracles subject to multiple vertex failures")
  Member "the 4 3 additive spanner exponent is tight":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weighted additive spanners")
  Member "improved purely additive fault tolerant spanners":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph spanners a tutorial review")
  Member "fault tolerant approximate shortest path trees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate distance oracles subject to multiple vertex failures")
  Member "fault tolerant approximate bfs structures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate distance oracles subject to multiple vertex failures")

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)