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: "a short note on compressed sensing with partially known signal support"
  Abstract: This short note studies a variation of the Compressed Sensing paradigm introduced recently by Vaswani et al., i.e. the recovery of sparse signals from a certain number of linear measurements when the signal support is partially known. The reconstruction method is based on a convex minimization program coined "innovative Basis Pursuit DeNoise" (or iBPDN). Under the common $\ell_2$-fidelity constraint made on the available measurements, this optimization promotes the ($\ell_1$) sparsity of the candidate signal over the complement of this known part. In particular, this paper extends the results of Vaswani et al. to the cases of compressible signals and noisy measurements. Our proof relies on a small adaption of the results of Candes in 2008 for characterizing the stability of the Basis Pursuit DeNoise (BPDN) program. We emphasize also an interesting link between our method and the recent work of Davenport et al. on the $\delta$-stable embeddings and the "cancel-then-recover" strategy applied to our problem. For both approaches, reconstructions are indeed stabilized when the sensing matrix respects the Restricted Isometry Property for the same sparsity order. We conclude by sketching an easy numerical method relying on monotone operator splitting and proximal methods that iteratively solves iBPDN.

Connected through hyperedge HE-1 (induced by source paper "rls recovery with asymmetric penalty fundamental limits and algorithmic approaches"):
  with members:
    - "precoding via approximate message passing with instantaneous signal constraints"
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "how to exploit prior information in low complexity models"):
  with members:
    - "collaborative total variation a general framework for vectorial tv models"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"):
  with members:
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "exact recovery conditions for sparse representations with partial support information"
    - "expectation maximization gaussian mixture approximate message passing"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "estimation of sparse mimo channels with common support"

Connected through hyperedge HE-4 (induced by source paper "time invariant error bounds for modified cs based sparse signal sequence recovery"):
  with members:
    - "on partial sparse recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "subspace methods for joint sparse recovery"
    - "exact dynamic support tracking with multiple measurement vectors using compressive music"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse signal reconstruction via iterative support detection"

Connected through hyperedge HE-5 (induced by source paper "exact recovery conditions for sparse representations with partial support information"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "sparse signal reconstruction via iterative support detection"
    - "recovering compressively sampled signals using partial support information"
    - "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-6 (induced by source paper "lorentzian iterative hard thresholding robust compressed sensing with prior information"):
  with members:
    - "recovery of sparsely corrupted signals"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "probabilistic recovery guarantees for sparsely corrupted signals"):
  with members:
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"
    - "recovery of sparsely corrupted signals"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "a probabilistic and ripless theory of compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-8 (induced by source paper "weighted ell_1 minimization with multiple weighting sets"):
  with members:
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "sparse recovery by non convex optimization instance optimality"

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)