human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "rank minimization for snapshot compressive imaging"
  Abstract: Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.

Connected through hyperedge HE-1 (induced by source paper "snapshot compressed sensing performance bounds and algorithms"):
  with members:
    - "an efficient algorithm for compression based compressed sensing"
    - "deep fully connected networks for video compressive sensing"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "compressive hyperspectral imaging with side information"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "deepbinarymask learning a binary mask for video compressive sensing"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "snapshot compressed sensing performance bounds and algorithms"
    - "deep fully connected networks for video compressive sensing"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "context encoders feature learning by inpainting"
    - "learning sensor multiplexing design through back propagation"

Question: Please tell me which class the center node 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)