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: "diffusercam lensless single exposure 3d imaging"
  Abstract: We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging. Our lensless system consists solely of a diffuser placed in front of a standard image sensor. Every point within the volumetric field-of-view projects a unique pseudorandom pattern of caustics on the sensor. By using a physical approximation and simple calibration scheme, we solve the large-scale inverse problem in a computationally efficient way. The caustic patterns enable compressed sensing, which exploits sparsity in the sample to solve for more 3D voxels than pixels on the 2D sensor. Our 3D voxel grid is chosen to match the experimentally measured two-point optical resolution across the field-of-view, resulting in 100 million voxels being reconstructed from a single 1.3 megapixel image. However, the effective resolution varies significantly with scene content. Because this effect is common to a wide range of computational cameras, we provide new theory for analyzing resolution in such systems.

Connected through hyperedge HE-1 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning"):
  with members:
    - "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram"
    - "resolution enhancement in scanning electron microscopy using deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "deep learning enhanced mobile phone microscopy"
    - "deep learning based super resolution in coherent imaging systems"
    - "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks"
    - "deep learning microscopy"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "learning to anonymize faces for privacy preserving action detection"
    - "what have we learned from deep representations for action recognition"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-3 (induced by source paper "learning based image reconstruction via parallel proximal algorithm"):
  with members:
    - "learning convex regularizers for optimal bayesian denoising"
    - "deep sparse coding using optimized linear expansion of thresholds"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "sparse stochastic processes and discretization of linear inverse problems"

Connected through hyperedge HE-4 (induced by source paper "toward depth estimation using mask based lensless cameras"):
  with members:
    - "flatcam thin bare sensor cameras using coded aperture and computation"
    - "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)