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: "fpa cs focal plane array based compressive imaging in short wave infrared"
  Abstract: Cameras for imaging in short and mid-wave infrared spectra are significantly more expensive than their counterparts in visible imaging. As a result, high-resolution imaging in those spectrum remains beyond the reach of most consumers. Over the last decade, compressive sensing (CS) has emerged as a potential means to realize inexpensive short-wave infrared cameras. One approach for doing this is the single-pixel camera (SPC) where a single detector acquires coded measurements of a high-resolution image. A computational reconstruction algorithm is then used to recover the image from these coded measurements. Unfortunately, the measurement rate of a SPC is insufficient to enable imaging at high spatial and temporal resolutions. #R##N#We present a focal plane array-based compressive sensing (FPA-CS) architecture that achieves high spatial and temporal resolutions. The idea is to use an array of SPCs that sense in parallel to increase the measurement rate, and consequently, the achievable spatio-temporal resolution of the camera. We develop a proof-of-concept prototype in the short-wave infrared using a sensor with 64$\times$ 64 pixels; the prototype provides a 4096$\times$ increase in the measurement rate compared to the SPC and achieves a megapixel resolution at video rate using CS techniques.

Connected through hyperedge HE-1 (induced by source paper "solving inverse computational imaging problems using deep pixel level prior"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "lensless computational imaging through deep learning"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "learning deep cnn denoiser prior for image restoration"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-2 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"
    - "on partial sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "deepbinarymask learning a binary mask for video compressive sensing"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep fully connected networks for video compressive sensing"):
  with members:
    - "compressive holographic video"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "learning to segment object candidates"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "lisens a scalable architecture for video compressive sensing"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "compressive image recovery using recurrent generative model"):
  with members:
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "adam a method for stochastic optimization"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "video compressive sensing for spatial multiplexing cameras using motion flow models"):
  with members:
    - "lisens a scalable architecture for video compressive sensing"
    - "compressive coded aperture keyed exposure imaging with optical flow reconstruction"
    - "coded aperture compressive temporal imaging"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-7 (induced by source paper "cross scale predictive dictionaries"):
  with members:
    - "fast sublinear sparse representation using shallow tree matching pursuit"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "lisens a scalable architecture for video compressive sensing"):
  with members:
    - "lensless imaging by compressive sensing"

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)