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 "multi resolution compressive sensing reconstruction"):
  Abstract: We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple regions, each one with a different resolution. This problem arises in situations where the image to reconstruct contains a certain region of interest (RoI) that is more important than the rest. Through a theoretical analysis and simulation experiments we show that the multi-resolution reconstruction provides a higher quality of the RoI compared to the traditional single-resolution approach.

Includes members:
  Member "multi resolution compressed sensing via approximate message passing":
    also connected through other hyperedges:
      (none)
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "lensless imaging by compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "the stone transform multi resolution image enhancement and real time compressive video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast integral image estimation at 1 measurement rate")
  Member "multi view in lensless compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lensless photography with only an image sensor")
  Member "minimum complexity pursuit for universal compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solving inverse problems via auto encoders")
  Member "surveillance video processing using compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.CV(Computer Vision and Pattern Recognition)