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 "inner imaging networks put lenses into convolutional structure"):
  Abstract: Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address this issue by enhancing diversities of filters, they have not considered the complementarity and the completeness of the internal structure of the convolutional network. To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intra-group and inter-group relationships simultaneously. The convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudo-image, like putting a lens into convolution internal structure. Consequently, not only the diversity of channels is increased, but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implemented. It provides an efficient self-organization strategy for convolutional networks so as to improve their efficiency and performance. Extensive experiments are conducted on multiple benchmark image recognition data sets including CIFAR, SVHN and ImageNet. Experimental results verify the effectiveness of the Inner-Imaging mechanism with the most popular convolutional networks as the backbones.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "dropblock a regularization method for convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "convolutional neural networks with alternately updated clique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "shufflenet v2 practical guidelines for efficient cnn architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "channelnets compact and efficient convolutional neural networks via channel wise convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural epitome search for architecture agnostic network compression")
  Member "training neural networks using features replay":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sideways depth parallel training of video models")
  Member "gather excite exploiting feature context in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)