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 "saliency preservation in low resolution grayscale images"):
  Abstract: Visual saliency detection originated over 500 million years ago and is one of the most efficient mechanisms in nature. In contrast, state-of-the-art computational saliency models are often complex and inefficient, primarily because they process high-resolution color (HC) images. Insights into the evolutionary origins of visual saliency detection suggest that achromatic low-resolution vision is essential to its speed and efficiency. Previous studies have shown that low-resolution color and high-resolution grayscale images preserve saliency information and are significantly more computationally attractive than HC images. Nevertheless, to our knowledge, no one has investigated whether salience is preserved in low-resolution grayscale (LG) images. In this study, we explain the biological and computational motivation for LG, and show, through eye-tracking experiments, that saliency information is preserved in LG images. We found high similarity between LG fixations from a group of observers and HC fixations from a benchmark group. We also found no significant difference in inter-observer congruency between LG and HC groups. We conclude by proposing LG images for fast and efficient saliency detection.

Includes members:
  Member "deep visual attention prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  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 "video salient object detection via fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "saliency revisited analysis of mouse movements versus fixations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection")
  Member "deepgaze ii reading fixations from deep features trained on object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "what do different evaluation metrics tell us about saliency models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "deepfix a fully convolutional neural network for predicting human eye fixations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tidying deep saliency prediction architectures")
  Member "cat2000 a large scale fixation dataset for boosting saliency research":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")

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)