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 "understanding learned cnn features through filter decoding with substitution"):
  Abstract: In parallel with the success of CNNs to solve vision problems, there is a growing interest in developing methodologies to understand and visualize the internal representations of these networks. How the responses of a trained CNN encode the visual information is a fundamental question both for computer and human vision research. Image representations provided by the first convolutional layer as well as the resolution change provided by the max-polling operation are easy to understand, however, as soon as a second and further convolutional layers are added in the representation, any intuition is lost. A usual way to deal with this problem has been to define deconvolutional networks that somehow allow to explore the internal representations of the most important activations towards the image space, where deconvolution is assumed as a convolution with the transposed filter. However, this assumption is not the best approximation of an inverse convolution. In this paper we propose a new assumption based on filter substitution to reverse the encoding of a convolutional layer. This provides us with a new tool to directly visualize any CNN single neuron as a filter in the first layer, this is in terms of the image space.

Includes members:
  Member "understanding neural networks through deep visualization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "understanding deep features with computer generated imagery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretable cnns for object classification")
  Member "understanding deep image representations by inverting them":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crl class representative learning for image classification")

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)