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: "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"
  Abstract: Systems based on bag-of-words models from image features collected at maxima of sparse interest point operators have been used successfully for both computer visual object and action recognition tasks. While the sparse, interest-point based approach to recognition is not inconsistent with visual processing in biological systems that operate in `saccade and fixate' regimes, the methodology and emphasis in the human and the computer vision communities remains sharply distinct. Here, we make three contributions aiming to bridge this gap. First, we complement existing state-of-the art large scale dynamic computer vision annotated datasets like Hollywood-2 and UCF Sports with human eye movements collected under the ecological constraints of the visual action recognition task. To our knowledge these are the first large human eye tracking datasets to be collected and made publicly available for video, vision.imar.ro/eyetracking (497,107 frames, each viewed by 16 subjects), unique in terms of their (a) large scale and computer vision relevance, (b) dynamic, video stimuli, (c) task control, as opposed to free-viewing. Second, we introduce novel sequential consistency and alignment measures, which underline the remarkable stability of patterns of visual search among subjects. Third, we leverage the significant amount of collected data in order to pursue studies and build automatic, end-to-end trainable computer vision systems based on human eye movements. Our studies not only shed light on the differences between computer vision spatio-temporal interest point image sampling strategies and the human fixations, as well as their impact for visual recognition performance, but also demonstrate that human fixations can be accurately predicted, and when used in an end-to-end automatic system, leveraging some of the advanced computer vision practice, can lead to state of the art results.

Connected through hyperedge HE-1 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network"):
  with members:
    - "reppoints point set representation for object detection"
    - "simple vs complex temporal recurrences for video saliency prediction"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "tdan temporally deformable alignment network for video super resolution"

Connected through hyperedge HE-2 (induced by source paper "unified image and video saliency modeling"):
  with members:
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "tased net temporally aggregating spatial encoder decoder network for video saliency detection"
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"

Connected through hyperedge HE-3 (induced by source paper "human vs machine attention in neural networks a comparative study"):
  with members:
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "reciprocal attention fusion for visual question answering"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "fine grained video categorization with redundancy reduction attention"
    - "reverse attention for salient object detection"
    - "learn to pay attention"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-4 (induced by source paper "symbiotic graph neural networks for 3d skeleton based human action recognition and motion prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "human motion prediction via learning local structure representations and temporal dependencies"
    - "a neural temporal model for human motion prediction"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "convolutional sequence to sequence model for human dynamics"
    - "look into person joint body parsing pose estimation network and a new benchmark"

Connected through hyperedge HE-5 (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-6 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"

Connected through hyperedge HE-7 (induced by source paper "salgaze personalizing gaze estimation using visual saliency"):
  with members:
    - "appearance based gaze estimation via gaze decomposition and single gaze point calibration"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deep pictorial gaze estimation"
    - "appearance based 3d gaze estimation with personal calibration"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"

Connected through hyperedge HE-8 (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"

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)