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 "snapshot compressed sensing performance bounds and algorithms"):
  Abstract: Snapshot compressed sensing (CS) refers to compressive imaging systems where multiple frames are mapped into a single measurement frame. Each pixel in the acquired frame is a linear combination of the corresponding pixels in the frames that are collapsed together. While the problem can be cast as a CS problem, due to the very special structure of the sensing matrix, standard CS theory cannot be employed to study such systems. In this paper, a compression-based framework is proposed that enable a theoretical analysis of snapshot compressive sensing systems. This new framework leads to two novel, computationally-efficient and theoretically-analyzable snapshot compressive sensing recovery algorithms. The proposed algorithms are iterative and employ compression codes to impose structure on the recovered frames. Theoretical convergence guarantees are derived for both algorithms. In simulations, it is shown that combining the proposed algorithms with a customized video compression code designed to exploit nonlocal structures of video frames significantly improves the state-of-the-art performance.

Includes members:
  Member "rank minimization for snapshot compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepbinarymask learning a binary mask for video compressive sensing")
  Member "an efficient algorithm for compression based compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms")
  Member "deep fully connected networks for video compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive image sampling using deep learning and its application on x ray fluorescence image reconstruction")
  Member "csvideonet a real time end to end learning framework for high frame rate video compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time object detection and localization in compressive sensed video on embedded hardware")
  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 "compressed sensing with structured sparsity and structured acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "video compressive sensing for spatial multiplexing cameras using motion flow models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recovery of structured signals with prior information via maximizing correlation")
  Member "compressive hyperspectral imaging with side information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rank minimization for snapshot compressive 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.IT(Information Theory)