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: "improving few shot user specific gaze adaptation via gaze redirection synthesis"
  Abstract: As an indicator of human attention gaze is a subtle behavioral cue which can be exploited in many applications. However, inferring 3D gaze direction is challenging even for deep neural networks given the lack of large amount of data (groundtruthing gaze is expensive and existing datasets use different setups) and the inherent presence of gaze biases due to person-specific difference. In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples. The main and novel idea is to improve gaze adaptation by generating additional training samples through the synthesis of gaze-redirected eye images from existing reference samples. In doing so, our contributions are threefold: (i) we design our gaze redirection framework from synthetic data, allowing us to benefit from aligned training sample pairs to predict accurate inverse mapping fields; (ii) we proposed a self-supervised approach for domain adaptation; (iii) we exploit the gaze redirection to improve the performance of person-specific gaze estimation. Extensive experiments on two public datasets demonstrate the validity of our gaze retargeting and gaze estimation framework.

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"
    - "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"
    - "light weight head pose invariant gaze tracking"

Connected through hyperedge HE-2 (induced by source paper "a generalized and robust method towards practical gaze estimation on smart phone"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "few shot adaptive gaze estimation"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "snapshot distillation teacher student optimization in one generation"
    - "feature denoising for improving adversarial robustness"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "repr improved training of convolutional filters"

Connected through hyperedge HE-3 (induced by source paper "a differential approach for gaze estimation"):
  with members:
    - "efficient eye typing with 9 direction gaze estimation"
    - "from depth data to head pose estimation a siamese approach"
    - "learning to compare relation network for few shot learning"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "3d gaze estimation from 2d pupil positions on monocular head mounted eye trackers"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

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)