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: "recovering compressively sampled signals using partial support information"
  Abstract: In this paper we study recovery conditions of weighted $\ell_1$ minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that if at least 50% of the (partial) support information is accurate, then weighted $\ell_1$ minimization is stable and robust under weaker conditions than the analogous conditions for standard $\ell_1$ minimization. Moreover, weighted $\ell_1$ minimization provides better bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal to be recovered. We illustrate our results with extensive numerical experiments on synthetic data and real audio and video signals.

Connected through hyperedge HE-1 (induced by source paper "exploiting prior information in block sparse signals"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "performance limits for noisy multimeasurement vector problems"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "spectral super resolution with prior knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information"):
  with members:
    - "signal recovery under cumulative coherence"
    - "recovery of signals under the condition on ric and roc via prior support information"
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-3 (induced by source paper "sparse subspace clustering via two step reweighted l1 minimization algorithm and provable neighbor recovery rates"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "iteratively reweighted ell_1 approaches to sparse composite regularization"
    - "robust subspace clustering"
    - "greedy feature selection for subspace clustering"
    - "a geometric analysis of subspace clustering with outliers"
    - "lasso screening rules via dual polytope projection"
    - "improving the thresholds of sparse recovery an analysis of a two step reweighted basis pursuit algorithm"

Connected through hyperedge HE-4 (induced by source paper "fusion based cooperative support identification for compressive networked sensing"):
  with members:
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-5 (induced by source paper "phaseless compressive sensing using partial support information"):
  with members:
    - "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"
    - "recovery analysis for weighted mixed ell_2 ell_p minimization with 0 p leq 1"
    - "recovery of signals under the high order rip condition via prior support information"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "phase retrieval for sparse signals"
    - "gespar efficient phase retrieval of sparse signals"

Connected through hyperedge HE-6 (induced by source paper "a sharp recovery condition for sparse signals with partial support information via orthogonal matching pursuit"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "recovery of signals under the high order rip condition via prior support information"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "online recovery guarantees and analytical results for omp"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"

Connected through hyperedge HE-7 (induced by source paper "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"):
  with members:
    - "learning co sparse analysis operators with separable structures"
    - "precise phase transition of total variation minimization"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "on the effective measure of dimension in the analysis cosparse model"
    - "projection onto the cosparse set is np hard"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-8 (induced by source paper "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "achieving autonomous compressive spectrum sensing for cognitive radios"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "estimating unknown sparsity in compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)