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 "convergence of the generalized alternating projection algorithm for compressive sensing"):
  Abstract: The convergence of the generalized alternating projection (GAP) algorithm is studied in this paper to solve the compressive sensing problem $\yv = \Amat \xv + \epsilonv$. By assuming that $\Amat\Amat\ts$ is invertible, we prove that GAP converges linearly within a certain range of step-size when the sensing matrix $\Amat$ satisfies restricted isometry property (RIP) condition of $\delta_{2K}$, where $K$ is the sparsity of $\xv$. The theoretical analysis is extended to the adaptively iterative thresholding (AIT) algorithms, for which the convergence rate is also derived based on $\delta_{2K}$ of the sensing matrix. We further prove that, under the same conditions, the convergence rate of GAP is faster than that of AIT. Extensive simulation results confirm the theoretical assertions.

Includes members:
  Member "lensless compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration")
  Member "compressive hyperspectral imaging with side information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "tree structure bayesian compressive sensing for video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg")
  Member "lensless imaging by compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "multi view in lensless compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lensless photography with only an image sensor")

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)