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 "sparse convolutional networks using the permutohedral lattice"):
  Abstract: This paper introduces an efficient, non-linear image adaptive filtering as a generalization of the standard spatial convolution of convolutional neural networks (CNNs). We build on the bilateral filtering operation, a commonly used edge-aware image processing technique. Our implementation of bilateral filters uses specialized data structures, and in this paper we demonstrate how these lead to generalizations and make the filters amendable to learning. This development enriches the convolutional operation found in CNNs, which becomes image adaptive, can process sparse input data, and produce continuous output. Our result also generalizes a class of densely connected graphical models with tractable mean field inference. It has previously been shown that mean field approximations in the subclass of models with Gaussian edge potentials reduce to a bilateral filtering operation. Here, we generalize this to the non-Gaussian case and allow highly parameterized potential functions. A diverse set of experiments validates the empirical performance and highlights the different aspects of the proposed operation.

Includes members:
  Member "convolutional feature masking for joint object and stuff segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive binarization based on fuzzy integrals")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "semantic image segmentation with deep convolutional nets and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "simultaneous detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "feedforward semantic segmentation with zoom out features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "texture superpixel clustering from patch based nearest neighbor matching")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning graphical model parameters with approximate marginal inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")

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)