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 "contextual encoder decoder network for visual saliency prediction"):
  Abstract: Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at explaining human fixation maps do not incorporate such a mechanism explicitly. Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. The architecture forms an encoder-decoder structure and includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, we combine the resulting representations with global scene information for accurately predicting visual saliency. Our model achieves competitive results on two public saliency benchmarks and we demonstrate the effectiveness of the suggested approach on selected examples. The network is based on a lightweight image classification backbone and hence presents a suitable choice for applications with limited computational resources to estimate human fixations across complex natural scenes.

Includes members:
  Member "influence of image classification accuracy on saliency map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tidying deep saliency prediction architectures")
  Member "predicting human eye fixations via an lstm based saliency attentive model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how much position information do convolutional neural networks encode")
  Member "saliency prediction in the deep learning era an empirical investigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unified image and video saliency modeling")
  Member "invariance analysis of saliency models versus human gaze during scene free viewing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges")
  Member "eml net an expandable multi layer network for saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how much position information do convolutional neural networks encode")
  Member "end to end saliency mapping via probability distribution prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "saliency benchmarking made easy separating models maps and metrics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tidying deep saliency prediction architectures")
  Member "dual path networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional networks with dense connectivity")

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)