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: "light weight head pose invariant gaze tracking"
  Abstract: Unconstrained remote gaze tracking using off-the-shelf cameras is a challenging problem. Recently, promising algorithms for appearance-based gaze estimation using convolutional neural networks (CNN) have been proposed. Improving their robustness to various confounding factors including variable head pose, subject identity, illumination and image quality remain open problems. In this work, we study the effect of variable head pose on machine learning regressors trained to estimate gaze direction. We propose a novel branched CNN architecture that improves the robustness of gaze classifiers to variable head pose, without increasing computational cost. We also present various procedures to effectively train our gaze network including transfer learning from the more closely related task of object viewpoint estimation and from a large high-fidelity synthetic gaze dataset, which enable our ten times faster gaze network to achieve competitive accuracy to its current state-of-the-art direct competitor.

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 "a coarse to fine adaptive network for appearance based gaze estimation"):
  with members:
    - "few shot adaptive gaze estimation"
    - "unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection"
    - "attention is all you need"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "appearance based gaze estimation in the wild"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "offset calibration for appearance based gaze estimation via gaze decomposition"):
  with members:
    - "appearance based gaze estimation using dilated convolutions"
    - "deep pictorial gaze estimation"
    - "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-4 (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"
    - "deep pictorial gaze estimation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "self normalizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "appearance based gaze estimation using dilated convolutions"):
  with members:
    - "using variable dwell time to accelerate gaze based web browsing with two step selection"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "dilated residual networks"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "understanding convolution for semantic segmentation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "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 "accurate and robust eye contact detection during everyday mobile device interactions"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"

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)