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 "concurrently extrapolating and interpolating networks for continuous model generation"):
  Abstract: Most deep image smoothing operators are always trained repetitively when different explicit structure-texture pairs are employed as label images for each algorithm configured with different parameters. This kind of training strategy often takes a long time and spends equipment resources in a costly manner. To address this challenging issue, we generalize continuous network interpolation as a more powerful model generation tool, and then propose a simple yet effective model generation strategy to form a sequence of models that only requires a set of specific-effect label images. To precisely learn image smoothing operators, we present a double-state aggregation (DSA) module, which can be easily inserted into most of current network architecture. Based on this module, we design a double-state aggregation neural network structure with a local feature aggregation block and a nonlocal feature aggregation block to obtain operators with large expression capacity. Through the evaluation of many objective and visual experimental results, we show that the proposed method is capable of producing a series of continuous models and achieves better performance than that of several state-of-the-art methods for image smoothing.

Includes members:
  Member "bi directional cascade network for perceptual edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "cfsnet toward a controllable feature space for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated texture cnn for efficient and configurable image denoising")
  Member "pixel adaptive convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "devil is in the edges learning semantic boundaries from noisy annotations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "modulating image restoration with continual levels via adaptive feature modification layers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gated texture cnn for efficient and configurable image denoising")
  Member "image super resolution by neural texture transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extended feature pyramid network for small object detection")
  Member "a general decoupled learning framework for parameterized image operators":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a generalized framework for edge preserving and structure preserving image smoothing")
  Member "a benchmark for edge preserving image smoothing":
    also connected through other hyperedges:
      (none)

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)