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 "image compression based on compressive sensing end to end comparison with jpeg"):
  Abstract: We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to jointly control the quantization step and compression ratio in order to achieve near optimal quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g. the single pixel camera, to construct a hardware compressive sampling system.

Includes members:
  Member "nonlocal low rank tensor factor analysis for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust subspace discovery by block diagonal adaptive locality constrained representation")
  Member "block wise lensless compressive camera":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration")
  Member "fundamental distortion limits of analog to digital compression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "distortion rate function of sub nyquist sampled gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analog to digital compression a new paradigm for converting signals to bits")
  Member "generalized alternating projection based total variation minimization for compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "compressive hyperspectral imaging with side information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "generalized alternating projection based total variation minimization for compressive sensing")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")

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)