human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "approximating sparsest cut in low rank graphs via embeddings from approximately low dimensional spaces"):
  with members:
    - "improved arv rounding in small set expanders and graphs of bounded threshold rank"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "a prg for lipschitz functions of polynomials with applications to sparsest cut"
    - "approximating non uniform sparsest cut via generalized spectra"

Connected through hyperedge HE-2 (induced by source paper "embedding approximately low dimensional ell_2 2 metrics into ell_1"):
  with members:
    - "improved arv rounding in small set expanders and graphs of bounded threshold rank"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating non uniform sparsest cut via generalized spectra"
    - "a log n omega 1 integrality gap for the sparsest cut sdp"

Question: Please tell me which class the center node 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)