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 "deep structured features for semantic segmentation"):
  Abstract: We propose a highly structured neural network architecture for semantic segmentation of images that combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. Thanks to its high degree of structure, our architecture has a very small memory footprint and thus fits onto low-power embedded and mobile platforms. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that the accuracy of our architecture is competitive with conventional pixel classification CNNs. Furthermore, we demonstrate that the proposed architecture is data efficient in the sense of matching the accuracy of pixel classification CNNs when trained on a much smaller data set.

Includes members:
  Member "dense semantic labeling of subdecimeter resolution images with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "recycling randomness with structure for sublinear time kernel expansions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications")
  Member "fully convolutional networks for dense semantic labelling of high resolution aerial imagery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "energy efficient machine learning in silicon a communications inspired approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy reliability limits in nanoscale feedforward neural networks and formulas")
  Member "discrete deep feature extraction a theory and new architectures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gabor frames and deep scattering networks in audio processing")
  Member "learning both weights and connections for efficient neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "a mathematical theory of deep convolutional neural networks for feature extraction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the role of invariance in spectral complexity based generalization bounds")

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)