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: "recurrent mixture density network for spatiotemporal visual attention"
  Abstract: In many computer vision tasks, the relevant information to solve the problem at hand is mixed to irrelevant, distracting information. This has motivated researchers to design attentional models that can dynamically focus on parts of images or videos that are salient, e.g., by down-weighting irrelevant pixels. In this work, we propose a spatiotemporal attentional model that learns where to look in a video directly from human fixation data. We model visual attention with a mixture of Gaussians at each frame. This distribution is used to express the probability of saliency for each pixel. Time consistency in videos is modeled hierarchically by: 1) deep 3D convolutional features to represent spatial and short-term time relations and 2) a long short-term memory network on top that aggregates the clip-level representation of sequential clips and therefore expands the temporal domain from few frames to seconds. The parameters of the proposed model are optimized via maximum likelihood estimation using human fixations as training data, without knowledge of the action in each video. Our experiments on Hollywood2 show state-of-the-art performance on saliency prediction for video. We also show that our attentional model trained on Hollywood2 generalizes well to UCF101 and it can be leveraged to improve action classification accuracy on both datasets.

Connected through hyperedge HE-1 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception"):
  with members:
    - "real time model based image color correction for underwater robots"
    - "deep learning for single image super resolution a brief review"
    - "feedback network for image super resolution"
    - "fast underwater image enhancement for improved visual perception"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "fast and accurate single image super resolution via information distillation network"

Connected through hyperedge HE-2 (induced by source paper "machine vision for improved human robot cooperation in adverse underwater conditions"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "fast underwater image enhancement for improved visual perception"
    - "robot to robot relative pose estimation using humans as markers"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "underwater surveying via bearing only cooperative localization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "3g structure for image caption generation"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "hierarchical multiscale recurrent neural networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "tased net temporally aggregating spatial encoder decoder network for video saliency detection"):
  with members:
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "the kinetics human action video dataset"
    - "salgan visual saliency prediction with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "simple vs complex temporal recurrences for video saliency prediction"):
  with members:
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "youtube vos sequence to sequence video object segmentation"
    - "excitation backprop for rnns"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "spatio temporal saliency networks for dynamic saliency prediction"

Connected through hyperedge HE-6 (induced by source paper "human visual attention prediction boosts learning performance of autonomous driving agents"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "agil learning attention from human for visuomotor tasks"
    - "gaze based context aware robotic system for assisted reaching and grasping"
    - "on offline evaluation of vision based driving models"
    - "attention is all you need"
    - "carla an open urban driving simulator"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-7 (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"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

Connected through hyperedge HE-8 (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"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "vanishing point detection with convolutional neural networks"
    - "generating videos with scene dynamics"
    - "the evolution of first person vision methods a survey"
    - "adam a method for stochastic optimization"

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)