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: "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
  Abstract: Weighted $\ell_1$-minimization has been studied as a technique for the reconstruction of a sparse signal from compressively sampled measurements when prior information about the signal, in the form of a support estimate, is available. In this work, we study the recovery conditions and the associated recovery guarantees of weighted $\ell_1$-minimization when arbitrarily many distinct weights are permitted. For example, such a setup might be used when one has multiple estimates for the support of a signal, and these estimates have varying degrees of accuracy. Our analysis yields an extension to existing works that assume only a single support estimate set upon which a constant weight is applied. We include numerical experiments, with both synthetic signals and real video data, that demonstrate the benefits of allowing non-uniform weights in the reconstruction procedure.

Connected through hyperedge HE-1 (induced by source paper "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"):
  with members:
    - "a sharp bound on ric in generalized orthogonal matching pursuit"
    - "recovery of signals under the high order rip condition via prior support information"
    - "the high order block rip condition for signal recovery"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "generalized orthogonal matching pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-2 (induced by source paper "a sharp recovery condition for sparse signals with partial support information via orthogonal matching pursuit"):
  with members:
    - "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"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-3 (induced by source paper "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"):
  with members:
    - "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"
    - "recovering compressively sampled signals using partial support information"
    - "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)