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: "lisens a scalable architecture for video compressive sensing"
  Abstract: The measurement rate of cameras that take spatially multiplexed measurements by using spatial light modulators (SLM) is often limited by the switching speed of the SLMs. This is especially true for single-pixel cameras where the photodetector operates at a rate that is many orders-of-magnitude greater than the SLM. We study the factors that determine the measurement rate for such spatial multiplexing cameras (SMC) and show that increasing the number of pixels in the device improves the measurement rate, but there is an optimum number of pixels (typically, few thousands) beyond which the measurement rate does not increase. This motivates the design of LiSens, a novel imaging architecture, that replaces the photodetector in the single-pixel camera with a 1D linear array or a line-sensor. We illustrate the optical architecture underlying LiSens, build a prototype, and demonstrate results of a range of indoor and outdoor scenes. LiSens delivers on the promise of SMCs: imaging at a megapixel resolution, at video rate, using an inexpensive low-resolution sensor.

Connected through hyperedge HE-1 (induced by source paper "targeted nonlinear adversarial perturbations in images and videos"):
  with members:
    - "sparse adversarial perturbations for videos"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "the kinetics human action video dataset"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "end to end learning for self driving cars"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "deep image scaling up image recognition"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a framework for super resolution of scalable video via sparse reconstruction of residual frames"):
  with members:
    - "a new compressive video sensing framework for mobile broadcast"

Connected through hyperedge HE-4 (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-5 (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"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "video compressive sensing for spatial multiplexing cameras using motion flow models"):
  with members:
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "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 "fpa cs focal plane array based compressive imaging in short wave infrared"):
  with members:
    - "compressive coded aperture keyed exposure imaging with optical flow reconstruction"
    - "coded aperture compressive temporal imaging"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed 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)