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 "decision propagation networks for image classification"):
  Abstract: High-level (e.g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e.g., color) features in the early layers underexplored. In this paper, we propose a novel Decision Propagation Module (DPM) to make an intermediate decision that could act as category-coherent guidance extracted from early layers, and then propagate it to the latter layers. Therefore, by stacking a collection of DPMs into a classification network, the generated Decision Propagation Network is explicitly formulated as to progressively encode more discriminative features guided by the decision, and then refine the decision based on the new generated features layer by layer. Comprehensive results on four publicly available datasets validate DPM could bring significant improvements for existing classification networks with minimal additional computational cost and is superior to the state-of-the-art methods.

Includes members:
  Member "cinic 10 is not imagenet or cifar 10":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dhog deep hierarchical object grouping")
  Member "ensemble soft margin softmax loss for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit semantic data augmentation for deep networks")
  Member "class2str end to end latent hierarchy learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal semi supervised semantic segmentation")
  Member "do convolutional neural networks learn class hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "deep forest towards an alternative to deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition")
  Member "deep convolutional decision jungle for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "training decision trees as replacement for convolution layers")
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "improving pairwise ranking for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "top 1 solution of multi moments in time challenge 2019")

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)