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 "generalized alternating projection based total variation minimization for compressive sensing"):
  Abstract: We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) algorithm. Extensive results demonstrate the high performance of proposed algorithm on compressive sensing, including two dimensional images, hyperspectral images and videos. We further derive the Alternating Direction Method of Multipliers (ADMM) framework with TV minimization for video and hyperspectral image compressive sensing under the CACTI and CASSI framework, respectively. Connections between GAP and ADMM are also provided.

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 "convergence of the generalized alternating projection algorithm for compressive sensing":
    also connected through other hyperedges:
      (none)
  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 "coded aperture compressive temporal imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "event driven video frame synthesis")
  Member "multi view in lensless compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lensless photography with only an image sensor")
  Member "surveillance video processing using compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the role and the importance of features for background modeling and foreground detection")

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)