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 "stacked u nets a no frills approach to natural image segmentation"):
  Abstract: Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and downsampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper network architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.

Includes members:
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "octnetfusion learning depth fusion from data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "large kernel matters improve semantic segmentation by global convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deformable convolutional networks":
    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 "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spgnet semantic prediction guidance for scene parsing")
  Member "visualizing the loss landscape of neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")

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)