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: "learning to personalize in appearance based gaze tracking"
  Abstract: In this paper, we investigate how to efficiently combine appearance-based methods and personal calibration for video-based gaze estimation. Previous calibration methods have learned an average model for all persons and then adapted it for a specific person. In contrast, we directly learn a model for personalized gaze estimation where the personal variations are modeled as a low-dimensional latent parameter space. During training, the network learns an efficient way to leverage these parameters for estimating gaze. After training, little person-specific data is required to optimize these (few) parameters. In fact, by calibrating only six parameters per person, the accuracy can be improved by a factor of up to 2.5, and reaches the same level as person-specific models. #R##N#We assess the performance of our method on a large dataset, consisting of high resolution images taken from near-infrared cameras with active illumination. As it turns out, our method is the first appearance-based approach to gaze estimation to reach the same level of accuracy as involved model-based methods. We further demonstrate that it outperforms existing approaches on two datasets with relatively low-resolution images, MPIIGaze and GazeCapture. Finally, we show using a synthetic dataset created using UnityEyes tools, that our method efficiently predicts 3D gaze (origin and direction of gaze) without the need of explicitly 3D-annotated training data.

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"
    - "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"
    - "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 "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"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "self supervised learning of a facial attribute embedding from video"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "perceptual losses for real time style transfer and super resolution"

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)