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 "predicting video saliency using crowdsourced mouse tracking data"):
  Abstract: This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data. We designed a mouse-contingent video viewing system which simulates the viewers' peripheral vision based on the position of the mouse cursor. The system enables the use of mouse-tracking data recorded from an ordinary computer mouse as an alternative to real gaze fixations recorded by a more expensive eye-tracker. We developed a crowdsourcing system that enables the collection of such mouse-tracking data at large scale. Using the collected mouse-tracking data we showed that it can serve as an approximation of eye-tracking data. Moreover, trying to increase the efficiency of collected mouse-tracking data we proposed a novel deep neural network algorithm that improves the quality of mouse-tracking saliency maps.

Includes members:
  Member "improving video compression with deep visual attention models":
    also connected through other hyperedges:
      (none)
  Member "are all the frames equally important":
    also connected through other hyperedges:
      (none)
  Member "predicting human eye fixations via an lstm based saliency attentive model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how much position information do convolutional neural networks encode")
  Member "revisiting video saliency a large scale benchmark and a new model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "deepvs a deep learning based video saliency prediction approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "saliency prediction in the deep learning era an empirical investigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unified image and video saliency modeling")
  Member "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "turkeyes a web based toolbox for crowdsourcing attention data")
  Member "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network")

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)