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 "block wise lensless compressive camera"):
  Abstract: The existing lensless compressive camera ($\text{L}^2\text{C}^2$)~\cite{Huang13ICIP} suffers from low capture rates, resulting in low resolution images when acquired over a short time. In this work, we propose a new regime to mitigate these drawbacks. We replace the global-based compressive sensing used in the existing $\text{L}^2\text{C}^2$ by the local block (patch) based compressive sensing. We use a single sensor for each block, rather than for the entire image, thus forming a multiple but spatially parallel sensor $\text{L}^2\text{C}^2$. This new camera retains the advantages of existing $\text{L}^2\text{C}^2$ while leading to the following additional benefits: 1) Since each block can be very small, {\em e.g.}$~8\times 8$ pixels, we only need to capture $\sim 10$ measurements to achieve reasonable reconstruction. Therefore the capture time can be reduced significantly. 2) The coding patterns used in each block can be the same, therefore the sensing matrix is only of the block size compared to the entire image size in existing $\text{L}^2\text{C}^2$. This saves the memory requirement of the sensing matrix as well as speeds up the reconstruction. 3) Patch based image reconstruction is fast and since real time stitching algorithms exist, we can perform real time reconstruction. 4) These small blocks can be integrated to any desirable number, leading to ultra high resolution images while retaining fast capture rate and fast reconstruction. We develop multiple geometries of this block-wise $\text{L}^2\text{C}^2$ in this paper. We have built prototypes of the proposed block-wise $\text{L}^2\text{C}^2$ and demonstrated excellent results of real data.

Includes members:
  Member "generalized alternating projection based total variation minimization for compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "lensless compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "flatcam thin bare sensor cameras using coded aperture and computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward depth estimation using mask based lensless cameras")
  Member "lensless imaging by compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "event driven video frame synthesis")
  Member "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neumann networks for inverse problems in imaging")

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)