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 "iterative hard thresholding for weighted sparse approximation"):
  Abstract: Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of Restricted Isometry Property for the space of weighted sparse signals. Using these notions, we pose a best weighted sparse approximation problem, i.e. we seek structured sparse solutions to underdetermined systems of linear equations. Many computationally efficient greedy algorithms have been developed to solve the problem of best $s$-sparse approximation. The design of all of these algorithms employ a similar template of exploiting the RIP and computing projections onto the space of sparse vectors. We present an extension of the Iterative Hard Thresholding (IHT) algorithm to solve the weighted sparse approximation problem. This IHT extension employs a weighted analogue of the template employed by all greedy sparse approximation algorithms. Theoretical guarantees are presented and much of the original analysis remains unchanged and extends quite naturally. However, not all the theoretical analysis extends. To this end, we identify and discuss the barrier to extension. Much like IHT, our IHT extension requires computing a projection onto a non-convex space. However unlike IHT and other greedy methods which deal with the classical notion of sparsity, no simple method is known for computing projections onto these weighted sparse spaces. Therefore we employ a surrogate for the projection and analyze its empirical performance.

Includes members:
  Member "weighted l minimization for generalized non uniform sparse model":
    also connected through other hyperedges:
      (none)
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded 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)