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: "exact recovery for sparse signal via weighted l_1 minimization"
  Abstract: Numerical experiments in literature on compressed sensing have indicated that the reweighted $l_1$ minimization performs exceptionally well in recovering sparse signal. In this paper, we develop exact recovery conditions and algorithm for sparse signal via weighted $l_1$ minimization from the insight of the classical NSP (null space property) and RIC (restricted isometry constant) bound. We first introduce the concept of WNSP (weighted null space property) and reveal that it is a necessary and sufficient condition for exact recovery. We then prove that the RIC bound by weighted $l_1$ minimization is $\delta_{ak} 1$, $0<\gamma\leq1$ is determined by an optimization problem over the null space. When $\gamma< 1$ this bound is greater than $\sqrt{\frac{a-1}{a}}$ from $l_1$ minimization. In addition, we also establish the bound on $\delta_k$ and show that it can be larger than the sharp one 1/3 via $l_1$ minimization and also greater than 0.4343 via weighted $l_1$ minimization under some mild cases. Finally, we achieve a modified iterative reweighted $l_1$ minimization (MIRL1) algorithm based on our selection principle of weight, and the numerical experiments demonstrate that our algorithm behaves much better than $l_1$ minimization and iterative reweighted $l_1$ minimization (IRL1) algorithm.

Connected through hyperedge HE-1 (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-2 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information"):
  with members:
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "on the theorem of uniform recovery of random sampling matrices"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "recovering compressively sampled signals using partial support information"
    - "model based compressive sensing"
    - "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)