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 "guruswami sinop rounding without higher level lasserre"):
  Abstract: Guruswami and Sinop give a $O(1/\delta)$ approximation guarantee for the non-uniform Sparsest Cut problem by solving $O(r)$-level Lasserre semidefinite constraints, provided that the generalized eigenvalues of the Laplacians of the cost and demand graphs satisfy a certain spectral condition, namely, $\lambda_{r+1} \geq \Phi^{*}/(1-\delta)$. Their key idea is a rounding technique that first maps a vector-valued solution to $[0, 1]$ using appropriately scaled projections onto Lasserre vectors. In this paper, we show that similar projections and analysis can be obtained using only $\ell_{2}^{2}$ triangle inequality constraints. This results in a $O(r/\delta^{2})$ approximation guarantee for the non-uniform Sparsest Cut problem by adding only $\ell_{2}^{2}$ triangle inequality constraints to the usual semidefinite program, provided that the same spectral condition, $\lambda_{r+1} \geq \Phi^{*}/(1-\delta)$, holds.

Includes members:
  Member "rounding lasserre sdps using column selection and spectrum based approximation schemes for graph partitioning and quadratic ips":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on online supervised learning with applications to node classification in social networks")
  Member "towards a better approximation for sparsest cut":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rounding sum of squares relaxations")
  Member "faster sdp hierarchy solvers for local rounding algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating constraint satisfaction problems on high dimensional expanders")
  Member "approximating non uniform sparsest cut via generalized spectra":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix decompositions and sparse graph regularity")
  Member "optimal column based low rank matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "a log n omega 1 integrality gap for the sparsest cut sdp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strong reductions for extended formulations")

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)