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: "irls and slime mold equivalence and convergence"
  Abstract: In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of a slime mold (Physarum polycephalum). Both of these dynamics are geared towards finding a minimum l1-norm solution in an affine subspace. Despite its simplicity the convergence of the IRLS method has been shown only for a certain regularization of it and remains an important open problem. Our first result shows that the two dynamics are projections of the same dynamical system in higher dimensions. As a consequence, and building on the recent work on Physarum dynamics, we are able to prove convergence and obtain complexity bounds for a damped version of the IRLS algorithm.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares"):
  with members:
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "on a natural dynamics for linear programming"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "physarum can compute shortest paths"
    - "runtime guarantees for regression problems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-3 (induced by source paper "a laplacian approach to ell_1 norm minimization"):
  with members:
    - "two results on slime mold computations"
    - "physarum can compute shortest paths"
    - "optimization with sparsity inducing penalties"
    - "runtime guarantees for regression problems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-4 (induced by source paper "fast provably convergent irls algorithm for p norm linear regression"):
  with members:
    - "flows in almost linear time via adaptive preconditioning"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "on a natural dynamics for linear programming"
    - "algorithms for lipschitz learning on graphs"

Connected through hyperedge HE-5 (induced by source paper "convergence of the non uniform physarum dynamics"):
  with members:
    - "two results on slime mold computations"
    - "on a natural dynamics for linear programming"
    - "physarum can compute shortest paths"

Connected through hyperedge HE-6 (induced by source paper "two results on slime mold computations"):
  with members:
    - "on a natural dynamics for linear programming"
    - "physarum can compute shortest paths"

Connected through hyperedge HE-7 (induced by source paper "a distributed learning dynamics in social groups"):
  with members:
    - "ant inspired density estimation via random walks"
    - "human collective intelligence as distributed bayesian inference"
    - "distributed house hunting in ant colonies"
    - "stabilizing consensus with many opinions"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "decentralized online learning algorithms for opportunistic spectrum access"
    - "online distributed sensor selection"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-8 (induced by source paper "nearly tight bounds for sandpile transience on the grid"):
  with members:
    - "on a natural dynamics for linear programming"
    - "physarum can compute shortest paths"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

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)