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 "temporal saliency adaptation in egocentric videos"):
  Abstract: This  work  adapts  a  deep  neural  model  for  image  saliency#R##N#prediction to the temporal domain of egocentric video. We compute the#R##N#saliency  map  for  each  video  frame,  firstly  with  an  off-the-shelf  model#R##N#trained  from  static  images,  secondly  by  adding  a  a  convolutional  or#R##N#conv-LSTM layers trained with a dataset for video saliency prediction.#R##N#We study each configuration on EgoMon, a new dataset made of seven#R##N#egocentric  videos  recorded  by  three  subjects  in  both  free-viewing  and#R##N#task-driven set ups. Our results indicate that the temporal adaptation is#R##N#beneficial when the viewer is not moving and observing the scene from#R##N#a narrow field of view. Encouraged by this observation, we compute and#R##N#publish the saliency maps for the EPIC Kitchens dataset, in which view-#R##N#ers are cooking.

Includes members:
  Member "faster gaze prediction with dense networks and fisher pruning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "scaling egocentric vision the epic kitchens dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to learn words from visual scenes")
  Member "revisiting video saliency a large scale benchmark and a new model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "predicting gaze in egocentric video by learning task dependent attention transition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "salgan visual saliency prediction with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "detecting engagement in egocentric video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "digging deeper into egocentric gaze prediction")
  Member "spatio temporal saliency networks for dynamic saliency prediction":
    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)