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: "experimental comparison of single pixel imaging algorithms"
  Abstract: Single-pixel imaging (SPI) is a novel technique that captures 2D images using a photodiode, instead of conventional 2D array sensors. SPI has high signal-to-noise ratio, wide spectral range, low cost, and robustness to light scattering. Various algorithms have been proposed for SPI reconstruction, including linear correlation methods, the alternating projection (AP) method, and compressive sensing (CS) based methods. However, there has been no comprehensive review discussing respective advantages, which is important for SPI’s further applications and development. In this paper, we review and compare these algorithms in a unified reconstruction framework. We also propose two other SPI algorithms, including a conjugate gradient descent (CGD) based method and a Poisson maximum-likelihood-based method. Both simulations and experiments validate the following conclusions: to obtain comparable reconstruction accuracy, the CS-based total variation (TV) regularization method requires the fewest measurements and consumes the least running time for small-scale reconstruction, the CGD and AP methods run fastest in large-scale cases, and the TV and AP methods are the most robust to measurement noise. In a word, there are trade-offs in capture efficiency, computational complexity, and robustness to noise among different SPI algorithms. We have released our source code for non-commercial use.

Connected through hyperedge HE-1 (induced by source paper "does deep learning always outperform simple linear regression in optical imaging"):
  with members:
    - "review on optical image hiding and watermarking techniques"
    - "end to end deep learning of optical fiber communications"
    - "deep learning approach for fourier ptychography microscopy"
    - "computational ghost imaging using deep learning"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "solving fourier ptychographic imaging problems via neural network modeling and tensorflow"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Connected through hyperedge HE-2 (induced by source paper "fast object classification in single pixel imaging"):
  with members:
    - "computational ghost imaging using deep learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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)