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 "distributed iterative gating networks for semantic segmentation"):
  Abstract: In this paper, we present a canonical structure for controlling information flow in neural networks with an efficient feedback routing mechanism based on a strategy of Distributed Iterative Gating (DIGNet). The structure of this mechanism derives from a strong conceptual foundation and presents a light-weight mechanism for adaptive control of computation similar to recurrent convolutional neural networks by integrating feedback signals with a feed-forward architecture. In contrast to other RNN formulations, DIGNet generates feedback signals in a cascaded manner that implicitly carries information from all the layers above. This cascaded feedback propagation by means of the propagator gates is found to be more effective compared to other feedback mechanisms that use feedback from the output of either the corresponding stage or from the previous stage. Experiments reveal the high degree of capability that this recurrent approach with cascaded feedback presents over feed-forward baselines and other recurrent models for pixel-wise labeling problems on three challenging datasets, PASCAL VOC 2012, COCO-Stuff, and ADE20K.

Includes members:
  Member "gated multiple feedback network for image super resolution":
    also connected through other hyperedges:
      (none)
  Member "recurrent iterative gating networks for semantic segmentation":
    also connected through other hyperedges:
      (none)
  Member "large kernel matters improve semantic segmentation by global convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "object region mining with adversarial erasing a simple classification to semantic segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explainable deep classification models for domain generalization")
  Member "recurrent scene parsing with perspective understanding in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic flow for fast and accurate scene parsing")
  Member "convolutional networks with adaptive inference graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "iterative deep convolutional encoder decoder network for medical image segmentation":
    also connected through other hyperedges:
      (none)
  Member "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spgnet semantic prediction guidance for scene parsing")

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)