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: "low cost edge sensing for high quality demosaicking"
  Abstract: Digital cameras that use color filter arrays (CFA) entail a demosaicking procedure to form full RGB images. To digital camera industry, demosaicking speed is as important as demosaicking accuracy, because camera users have been accustomed to viewing captured photos instantly. Moreover, the cost associated with demosaicking should not go beyond the cost saved by using CFA. For this purpose, we revisit the classical Hamilton–Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Our analysis shows that the HA pipeline is highly efficient to exploit the originally captured data, but its oversimplified inter- and intra-channel smoothness formulation hinder its accuracy. Therefore, we propose a very low cost edge sensing scheme, which guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 27 demosaicking algorithms by running their open source code on benchmark datasets. Compared with the methods of similar computational cost, our method achieves substantially higher accuracy, whereas compared with the methods of similar accuracy, our method has significantly lower cost. On test images of currently popular resolution, the quality of our algorithm is comparable to top performers, yet our speed is tens of times faster. Source code is submitted to  http://ieeexplore.ieee.org .

Connected through hyperedge HE-1 (induced by source paper "cross channel correlation preserved three stream lightweight cnns for demosaicking"):
  with members:
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "matconvnet convolutional neural networks for matlab"

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)