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 "local algorithms for sparse spanning graphs"):
  Abstract: We initiate the study of the problem of designing sublinear-time (local) algorithms that, given an edge $(u,v)$ in a connected graph $G=(V,E)$, decide whether $(u,v)$ belongs to a sparse spanning graph $G' = (V,E')$ of $G$. Namely, $G'$ should be connected and $|E'|$ should be upper bounded by $(1+\epsilon)|V|$ for a given parameter $\epsilon > 0$. To this end the algorithms may query the incidence relation of the graph $G$, and we seek algorithms whose query complexity and running time (per given edge $(u,v)$) is as small as possible. Such an algorithm may be randomized but (for a fixed choice of its random coins) its decision on different edges in the graph should be consistent with the same spanning graph $G'$ and independent of the order of queries. #R##N#We first show that for general (bounded-degree) graphs, the query complexity of any such algorithm must be $\Omega(\sqrt{|V|})$. This lower bound holds for graphs that have high expansion. We then turn to design and analyze algorithms both for graphs with high expansion (obtaining a result that roughly matches the lower bound) and for graphs that are (strongly) non-expanding (obtaining results in which the complexity does not depend on $|V|$). The complexity of the problem for graphs that do not fall into these two categories is left as an open question.

Includes members:
  Member "local computation algorithms for spanners":
    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 "efficient algorithms for constructing very sparse spanners and emulators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "a local algorithm for constructing spanners in minor free graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local computation algorithms for spanners")
  Member "local computation algorithms for graphs of non constant degrees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "flow based algorithms for local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "a local computation approximation scheme to maximum matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed maximum matching verification in congest")
  Member "a simple online competitive adaptation of lempel ziv compression with efficient random access support":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs")
  Member "a quasi polynomial time partition oracle for graphs with an excluded minor":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local computation algorithms for 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)