human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "two stream convolutional networks for dynamic saliency prediction"
  Abstract: In recent years, visual saliency estimation in images has attracted much attention in the computer vision community. However, predicting saliency in videos has received rela- tively little attention. Inspired by the recent success of deep convolutional neural networks based static saliency mod- els, in this work, we study two different two-stream convo- lutional networks for dynamic saliency prediction. To im- prove the generalization capability of our models, we also introduce a novel, empirically grounded data augmenta- tion technique for this task. We test our models on DIEM dataset and report superior results against the existing mod- els. Moreover, we perform transfer learning experiments on SALICON, a recently proposed static saliency dataset, by finetuning our models on the optical flows estimated from static images. Our experiments show that taking motion into account in this way can be helpful for static saliency estimation.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"

Connected through hyperedge HE-2 (induced by source paper "predicting head movement in panoramic video a deep reinforcement learning approach"):
  with members:
    - "video salient object detection via fully convolutional networks"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

Connected through hyperedge HE-3 (induced by source paper "digging deeper into egocentric gaze prediction"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "first person action object detection with egonet"
    - "detecting engagement in egocentric video"
    - "vanishing point detection with convolutional neural networks"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "generating videos with scene dynamics"
    - "the evolution of first person vision methods a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "video salient object detection via fully convolutional networks"):
  with members:
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "learning video object segmentation from static images"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "personalizing human video pose estimation"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-5 (induced by source paper "modeling attention in panoramic video a deep reinforcement learning approach"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "recurrent models of visual attention"

Question: Please tell me which class the center node 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)