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: "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
  Abstract: This paper addresses the challenging problem of estimating the general visual attention of people in images. Our proposed method is designed to work across multiple naturalistic social scenarios and provides a full picture of the subject's attention and gaze. In contrast, earlier works on gaze and attention estimation have focused on constrained problems in more specific contexts. In particular, our model explicitly represents the gaze direction and handles out-of-frame gaze targets. We leverage three different datasets using a multi-task learning approach. We evaluate our method on widely used benchmarks for single-tasks such as gaze angle estimation and attention-within-an-image, as well as on the new challenging task of generalized visual attention prediction. In addition, we have created extended annotations for the MMDB and GazeFollow datasets which are used in our experiments, which we will publicly release.

Connected through hyperedge HE-1 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition"):
  with members:
    - "offset calibration for appearance based gaze estimation via gaze decomposition"
    - "photo realistic monocular gaze redirection using generative adversarial networks"
    - "appearance based gaze estimation using dilated convolutions"
    - "few shot adaptive gaze estimation"
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "a differential approach for gaze estimation"
    - "recurrent cnn for 3d gaze estimation using appearance and shape cues"
    - "light weight head pose invariant gaze tracking"

Connected through hyperedge HE-2 (induced by source paper "attention flow end to end joint attention estimation"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "teacher s perception in the classroom"
    - "cbam convolutional block attention module"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "supervising neural attention models for video captioning by human gaze data"
    - "residual attention network for image classification"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-3 (induced by source paper "detecting attended visual targets in video"):
  with members:
    - "laeo net revisiting people looking at each other in videos"
    - "extended gaze following detecting objects in videos beyond the camera field of view"
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (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"
    - "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"
    - "salgan visual saliency prediction with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "unsupervised representation learning for gaze estimation"):
  with members:
    - "gaze contingent ocular parallax rendering for virtual reality"
    - "appearance based gaze estimation via gaze decomposition and single gaze point calibration"
    - "openeds open eye dataset"
    - "a differential approach for gaze estimation"
    - "self supervised learning of a facial attribute embedding from video"
    - "learning to personalize in appearance based gaze tracking"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "appearance based gaze estimation via gaze decomposition and single gaze point calibration"):
  with members:
    - "using variable dwell time to accelerate gaze based web browsing with two step selection"
    - "appearance based gaze estimation using dilated convolutions"
    - "light weight head pose invariant gaze tracking"
    - "deep pictorial gaze estimation"
    - "appearance based 3d gaze estimation with personal calibration"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"

Connected through hyperedge HE-7 (induced by source paper "extended gaze following detecting objects in videos beyond the camera field of view"):
  with members:
    - "neural network based reinforcement learning for audio visual gaze control in human robot interaction"
    - "deep visual attention prediction"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "eye tracking for everyone"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "u net convolutional networks for biomedical image segmentation"
    - "appearance based gaze estimation in the wild"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-8 (induced by source paper "gaze estimation for assisted living environments"):
  with members:
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "convolutional pose machines"
    - "eye tracking for everyone"
    - "it s written all over your face full face appearance based gaze estimation"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "appearance based gaze estimation in the wild"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)