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: "deep pictorial gaze estimation"
  Abstract: Estimating human gaze from natural eye images only is a challenging task. Gaze direction can be defined by the pupil- and the eyeball center where the latter is unobservable in 2D images. Hence, achieving highly accurate gaze estimates is an ill-posed problem. In this paper, we introduce a novel deep neural network architecture specifically designed for the task of gaze estimation from single eye input. Instead of directly regressing two angles for the pitch and yaw of the eyeball, we regress to an intermediate pictorial representation which in turn simplifies the task of 3D gaze direction estimation. Our quantitative and qualitative results show that our approach achieves higher accuracies than the state-of-the-art and is robust to variation in gaze, head pose and image quality.

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"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "recurrent cnn for 3d gaze estimation using appearance and shape cues"

Connected through hyperedge HE-2 (induced by source paper "offset calibration for appearance based gaze estimation via gaze decomposition"):
  with members:
    - "appearance based gaze estimation using dilated convolutions"
    - "light weight head pose invariant gaze tracking"
    - "learning to personalize in appearance based gaze tracking"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"

Connected through hyperedge HE-3 (induced by source paper "few shot adaptive gaze estimation"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "on first order meta learning algorithms"
    - "few shot segmentation propagation with guided networks"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "light weight head pose invariant gaze tracking"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "self normalizing neural networks"

Connected through hyperedge HE-4 (induced by source paper "photo realistic monocular gaze redirection using generative adversarial networks"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "multimodal unsupervised image to image translation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "gazedirector fully articulated eye gaze redirection in video"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "towards a complete 3d morphable model of the human head"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "3d morphable face models past present and future"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "head reconstruction from internet photos"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-7 (induced by source paper "improving few shot user specific gaze adaptation via gaze redirection synthesis"):
  with members:
    - "recurrent cnn for 3d gaze estimation using appearance and shape cues"
    - "appearance based 3d gaze estimation with personal calibration"
    - "ganimation anatomically aware facial animation from a single image"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "gazedirector fully articulated eye gaze redirection in video"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-8 (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"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "light weight head pose invariant gaze tracking"
    - "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"

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)