human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "compressed sensing with prior information optimal strategies geometry and bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal dictionary learning for image separation with application in art investigation")
  Member "recovery analysis for weighted ell_1 minimization using a null space property":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recovery of signals under the condition on ric and roc via prior support information")
  Member "exact recovery for sparse signal via weighted l_1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phaseless compressive sensing using partial support information")
  Member "on the theorem of uniform recovery of random sampling matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization")
  Member "weighted ell_1 minimization with multiple weighting sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weighted matrix completion and recovery with prior subspace information")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for sparse time sequences")
  Member "recovering compressively sampled signals using partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting prior information in block sparse signals")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed sensing with invertible generative models and dependent noise")

Question: Please tell me which class the source paper 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)