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 "recurrent iterative gating networks for semantic segmentation"):
  Abstract: In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information in neural networks in a top-down manner, and different variants on the core structure are considered. The iterative nature of this mechanism allows for gating to spread in both spatial extent and feature space. This is revealed to be a powerful mechanism with broad compatibility with common existing networks. Analysis shows how gating interacts with different network characteristics, and we also show that more shallow networks with gating may be made to perform better than much deeper networks that do not include RIGNet modules.

Includes members:
  Member "gated feedback refinement network for coarse to fine dense semantic image labeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient ladder style densenets for semantic segmentation of large images")
  Member "on the iterative refinement of densely connected representation levels for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "learning with rethinking recurrently improving convolutional neural networks through feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed iterative gating networks for semantic segmentation")
  Member "label refinement network for coarse to fine semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepdualmapper a gated fusion network for automatic map extraction using aerial images and trajectories")
  Member "recurrent human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "geometric scene parsing with hierarchical lstm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview")

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)