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: "sparse recovery of positive signals with minimal expansion"
  Abstract: We investigate the sparse recovery problem of reconstructing a high-dimensional non-negative sparse vector from lower dimensional linear measurements. While much work has focused on dense measurement matrices, sparse measurement schemes are crucial in applications, such as DNA microarrays and sensor networks, where dense measurements are not practically feasible. One possible construction uses the adjacency matrices of expander graphs, which often leads to recovery algorithms much more efficient than $\ell_1$ minimization. However, to date, constructions based on expanders have required very high expansion coefficients which can potentially make the construction of such graphs difficult and the size of the recoverable sets small. #R##N#In this paper, we construct sparse measurement matrices for the recovery of non-negative vectors, using perturbations of the adjacency matrix of an expander graph with much smaller expansion coefficient. We present a necessary and sufficient condition for $\ell_1$ optimization to successfully recover the unknown vector and obtain expressions for the recovery threshold. For certain classes of measurement matrices, this necessary and sufficient condition is further equivalent to the existence of a "unique" vector in the constraint set, which opens the door to alternative algorithms to $\ell_1$ minimization. We further show that the minimal expansion we use is necessary for any graph for which sparse recovery is possible and that therefore our construction is tight. We finally present a novel recovery algorithm that exploits expansion and is much faster than $\ell_1$ optimization. Finally, we demonstrate through theoretical bounds, as well as simulation, that our method is robust to noise and approximate sparsity.

Connected through hyperedge HE-1 (induced by source paper "perfect recovery conditions for non negative sparse modeling"):
  with members:
    - "a unique nonnegative solution to an underdetermined system from vectors to matrices"

Connected through hyperedge HE-2 (induced by source paper "sharp mse bounds for proximal denoising"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "a framework to characterize performance of lasso algorithms"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "guarantees of total variation minimization for signal recovery"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing of block sparse positive vectors"):
  with members:
    - "lifting l q optimization thresholds"
    - "optimality of ell_2 ell_1 optimization block length dependent thresholds"
    - "regularly random duality"
    - "lifting l 1 optimization strong and sectional thresholds"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-4 (induced by source paper "optimality of ell_2 ell_1 optimization block length dependent thresholds"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "block length dependent thresholds in block sparse compressed sensing"
    - "simultaneously sparse solutions to linear inverse problems with multiple system matrices and a single observation vector"

Connected through hyperedge HE-5 (induced by source paper "sketching sparse matrices"):
  with members:
    - "online identification and tracking of subspaces from highly incomplete information"
    - "fast moment estimation in data streams in optimal space"
    - "robust principal component analysis"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-6 (induced by source paper "summary based structures with improved sublinear recovery for compressed sensing"):
  with members:
    - "structured sublinear compressive sensing via belief propagation"
    - "compressed sensing how sharp is the restricted isometry property"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-7 (induced by source paper "a unique nonnegative solution to an underdetermined system from vectors to matrices"):
  with members:
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-8 (induced by source paper "a simple message passing algorithm for compressed sensing"):
  with members:
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing ii analysis and validation"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

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.IT(Information Theory)