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 "is cheeger type approximation possible for nonuniform sparsest cut"):
  Abstract: In the {\em nonuniform sparsest cut} problem, given two undirected graphs $G$ and $H$ over the same set of vertices $V$, we want to find a cut $(S,V-S)$ that minimizes the ratio between the fraction of $G$-edges that are cut and the fraction of $H$-edges that are cut. The ratio (which is at most 1 in an optimal solution) is called the {\em sparsity} of the cut. In the {\em uniform sparsest cut} problem, $H$ is a clique over $V$. If $G$ is regular, it is possible to find a solution to the uniform sparsest cut of cost $O(\sqrt{opt})$ in nearly linear time. Is such an approximation, which we call "Cheege-type" approximation, achievable in the non-uniform case? #R##N#We show that the answer is negative, assuming the Unique Games Conjecture, for general H. Furthermore, the Leighton-Rao linear programming relaxation and the spectral relaxation fail to find such an approximation even if $H$ is a clique over a subset of vertices. Using semidefinite programming, however, we can find Cheeger-type approximations in polynomial time whenever the adjacency matrix of $H$ has rank 1. (This includes the cases in which $H$ is a clique over a subset of vertices.)

Includes members:
  Member "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spectral approach to network design")
  Member "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)