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: "improved arv rounding in small set expanders and graphs of bounded threshold rank"
  Abstract: We prove a structure theorem for the feasible solutions of the Arora-Rao-Vazirani SDP relaxation on low threshold rank graphs and on small-set expanders. We show that if G is a graph of bounded threshold rank or a small-set expander, then an optimal solution of the Arora-Rao-Vazirani relaxation (or of any stronger version of it) can be almost entirely covered by a small number of balls of bounded radius. #R##N#Then, we show that, if k is the number of balls, a solution of this form can be rounded with an approximation factor of O(sqrt {log k}) in the case of the Arora-Rao-Vazirani relaxation, and with a constant-factor approximation in the case of the k-th round of the Sherali-Adams hierarchy starting at the Arora-Rao-Vazirani relaxation. #R##N#The structure theorem and the rounding scheme combine to prove the following result, where G=(V,E) is a graph of expansion \phi(G), \lambda_k is the k-th smallest eigenvalue of the normalized Laplacian of G, and \phi_k(G) = \min_{disjoint S_1,...,S_k} \max_{1  > log^{2.5} k \cdot phi(G) or \phi_{k} (G) >> log k \cdot sqrt{log n}\cdot loglog n\cdot \phi(G), then the Arora-Rao-Vazirani relaxation can be rounded in polynomial time with an approximation ratio O(sqrt{log k}). #R##N#Stronger approximation guarantees are achievable in time exponential in k via relaxations in the Lasserre hierarchy. Guruswami and Sinop [GS13] and Arora, Ge and Sinop [AGS13] prove that 1+eps approximation is achievable in time 2^{O(k)} poly(n) if either \lambda_k > \phi(G)/ poly(eps), or if SSE_{n/k} > sqrt{log k log n} \cdot \phi(G)/ poly(eps), where SSE_s is the minimal expansion of sets of size at most s.

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:
    - "guruswami sinop rounding without higher level lasserre"
    - "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:
    - "guruswami sinop rounding without higher level lasserre"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards a better approximation for sparsest cut"):
  with members:
    - "many sparse cuts via higher eigenvalues"
    - "faster sdp hierarchy solvers for local rounding algorithms"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "min max graph partitioning and small set expansion"
    - "optimal column based low rank matrix reconstruction"
    - "efficient volume sampling for row column subset selection"

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)