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: "detecting engagement in egocentric video"
  Abstract: In a wearable camera video, we see what the camera wearer sees. While this makes it easy to know roughly what he chose to look at, it does not immediately reveal when he was engaged with the environment. Specifically, at what moments did his focus linger, as he paused to gather more information about something he saw? Knowing this answer would benefit various applications in video summarization and augmented reality, yet prior work focuses solely on the "what" question (estimating saliency, gaze) without considering the "when" (engagement). We propose a learning-based approach that uses long-term egomotion cues to detect engagement, specifically in browsing scenarios where one frequently takes in new visual information (e.g., shopping, touring). We introduce a large, richly annotated dataset for ego-engagement that is the first of its kind. Our approach outperforms a wide array of existing methods. We show engagement can be detected well independent of both scene appearance and the camera wearer's identity.

Connected through hyperedge HE-1 (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"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "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-2 (induced by source paper "from third person to first person dataset and baselines for synthesis and retrieval"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "cross view image synthesis using geometry guided conditional gans"
    - "identifying first person camera wearers in third person videos"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "egocentric meets top view"
    - "generative adversarial text to image synthesis"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "temporal saliency adaptation in egocentric videos"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "scaling egocentric vision the epic kitchens dataset"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "where is my phone personal object retrieval from egocentric images"

Connected through hyperedge HE-4 (induced by source paper "egocentric basketball motion planning from a single first person image"):
  with members:
    - "generating long term trajectories using deep hierarchical networks"
    - "first person action object detection with egonet"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding neural networks through deep visualization"
    - "predicting important objects for egocentric video summarization"
    - "understanding deep image representations by inverting them"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "using cross model egosupervision to learn cooperative basketball intention"):
  with members:
    - "first person action object detection with egonet"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "attention to scale scale aware semantic image segmentation"
    - "deep residual learning for image recognition"
    - "predicting important objects for egocentric video summarization"
    - "cross modal distillation for supervision transfer"

Connected through hyperedge HE-6 (induced by source paper "next active object prediction from egocentric videos"):
  with members:
    - "first person action object detection with egonet"
    - "simple online and realtime tracking"
    - "going deeper into first person activity recognition"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"
    - "compact cnn for indexing egocentric videos"
    - "you only look once unified real time object detection"
    - "predicting important objects for egocentric video summarization"
    - "anticipating visual representations from unlabeled video"

Connected through hyperedge HE-7 (induced by source paper "customizing first person image through desired actions"):
  with members:
    - "first person action object detection with egonet"
    - "going deeper into first person activity recognition"
    - "video summarization with long short term memory"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning image representations tied to ego motion"
    - "learning to see by moving"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "egocap egocentric marker less motion capture with two fisheye cameras"):
  with members:
    - "a versatile scene model with differentiable visibility applied to generative pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "going deeper into first person activity recognition"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "learning action maps of large environments via first person vision"
    - "general automatic human shape and motion capture using volumetric contour cues"

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)