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: "performance analysis for sparse support recovery"
  Abstract: The performance of estimating the common support for jointly sparse signals based on their projections onto lower-dimensional space is analyzed. Support recovery is formulated as a multiple-hypothesis testing problem. Both upper and lower bounds on the probability of error are derived for general measurement matrices, by using the Chernoff bound and Fano's inequality, respectively. The upper bound shows that the performance is determined by a quantity measuring the measurement matrix incoherence, while the lower bound reveals the importance of the total measurement gain. The lower bound is applied to derive the minimal number of samples needed for accurate direction-of-arrival (DOA) estimation for a sparse representation based algorithm. When applied to Gaussian measurement ensembles, these bounds give necessary and sufficient conditions for a vanishing probability of error for majority realizations of the measurement matrix. Our results offer surprising insights into sparse signal recovery. For example, as far as support recovery is concerned, the well-known bound in Compressive Sensing with the Gaussian measurement matrix is generally not sufficient unless the noise level is low. Our study provides an alternative performance measure, one that is natural and important in practice, for signal recovery in Compressive Sensing and other application areas exploiting signal sparsity.

Connected through hyperedge HE-1 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior"):
  with members:
    - "inference under information constraints i lower bounds from chi square contraction"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-2 (induced by source paper "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"):
  with members:
    - "on the restricted isometry of the columnwise khatri rao product"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "estimation of sparse mimo channels with common support"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "rank awareness in joint sparse recovery"
    - "distributed compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "greedy subspace pursuit for joint sparse recovery"):
  with members:
    - "joint sparse recovery with semisupervised music"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "generalized orthogonal matching pursuit"
    - "improving noise robustness in subspace based joint sparse recovery"
    - "subspace methods for joint sparse recovery"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "an information theoretic study for noisy compressed sensing with joint sparsity model 2"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "subspace recovery from structured union of subspaces"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "information theoretic bounds for compressed sensing"
    - "distributed compressive sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-5 (induced by source paper "limits on support recovery with probabilistic models an information theoretic framework"):
  with members:
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "signal estimation with additive error metrics in compressed sensing"
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-6 (induced by source paper "limits on support recovery with probabilistic models an information spectrum approach"):
  with members:
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "the capacity of adaptive group testing"
    - "support recovery with sparsely sampled free random matrices"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "joint sparsity pattern recovery with 1 bit compressive sensing in sensor networks"):
  with members:
    - "one bit compressed sensing by linear programming"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-8 (induced by source paper "discrimination on the grassmann manifold fundamental limits of subspace classifiers"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "learning robust subspace clustering"
    - "support recovery with sparsely sampled free random matrices"
    - "communications inspired linear discriminant analysis"
    - "on the capacity of large mimo block fading channels"

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)