human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fully point wise convolutional neural network for modeling statistical regularities in natural images"):
  Abstract: Modeling statistical regularities is the problem of representing the pixel distributions in natural images, and usually applied to solve the ill-posed image processing problems. In this paper, we present an extremely efficient CNN architecture for modeling statistical regularities. Our method is based on the observation that, by random sampling the pixels in natural images, we can obtain a set of pixel ensembles in which the pixel value is independent identically distributed. This leads to the idea of using 1*1 (point-wise) convolution kernel instead of k*k convolution kernel to learn the feature representation efficiently. Accordingly, we design a novel architecture with fully point-wise convolutions to greatly reduce the model complexity while maintaining the representation ability. Experiments on three applications: color constancy, image dehazing and underwater image enhancement demonstrate the superior performance of our proposed network over the existing architectures, i.e., using 1/10-1/100 network parameters and computational cost over the state-of-the-art networks while achieving comparable accuracy. Codes and models will be made publicly available.

Includes members:
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "large kernel matters improve semantic segmentation by global convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "an all in one network for dehazing and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "dehazenet an end to end system for single image haze removal":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "fast fourier color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "convolutional color constancy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a multi hypothesis approach to color constancy")

Question: Please tell me which class the source paper 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)