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 computation algorithms for graphs of non constant degrees"):
  Abstract: In the model of \emph{local computation algorithms} (LCAs), we aim to compute the queried part of the output by examining only a small (sublinear) portion of the input. Many recently developed LCAs on graph problems achieve time and space complexities with very low dependence on $n$, the number of vertices. Nonetheless, these complexities are generally at least exponential in $d$, the upper bound on the degree of the input graph. Instead, we consider the case where parameter $d$ can be moderately dependent on $n$, and aim for complexities with subexponential dependence on $d$, while maintaining polylogarithmic dependence on $n$. We present: a randomized LCA for computing maximal independent sets whose time and space complexities are quasi-polynomial in $d$ and polylogarithmic in $n$; for constant $\epsilon > 0$, a randomized LCA that provides a $(1-\epsilon)$-approximation to maximum matching whose time and space complexities are polynomial in $d$ and polylogarithmic in $n$.

Includes members:
  Member "flow based algorithms for local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "distributed maximum matching in bounded degree graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed maximum matching verification in congest")
  Member "new techniques and tighter bounds for local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "best of two local models local centralized and local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sublinear random access generators for preferential attachment graphs")
  Member "local algorithms for sparse spanning graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  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 "local computation mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local computation algorithms for the lovasz local lemma")

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)