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: "appearance based 3d gaze estimation with personal calibration"
  Abstract: We propose a way to incorporate personal calibration into a deep learning model for video-based gaze estimation. Using our method, we show that by calibrating six parameters per person, accuracy can be improved by a factor of 2.2 to 2.5. The number of personal parameters, three per eye, is similar to the number predicted by geometrical models. When evaluated on the MPIIGaze dataset, our estimator performs better than person-specific estimators. To improve generalization, we predict gaze rays in 3D (origin and direction of gaze). In existing datasets, the 3D gaze is underdetermined, since all gaze targets are in the same plane as the camera. Experiments on synthetic data suggest it would be possible to learn accurate 3D gaze from only annotated gaze targets, without annotated eye positions.

Connected through hyperedge HE-1 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality"):
  with members:
    - "ritnet real time semantic segmentation of the eye for gaze tracking"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "unsupervised learning of depth and ego motion from video"
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "eyenet a multi task network for off axis eye gaze estimation and user understanding"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "deep facial expression recognition a survey"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "unsupervised learning of depth and ego motion from video"
    - "eyemotion classifying facial expressions in vr using eye tracking cameras"
    - "eye tracking for everyone"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-4 (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"
    - "deep pictorial gaze estimation"
    - "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-5 (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"
    - "deep pictorial gaze estimation"
    - "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)