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: "few shot adaptive gaze estimation"
  Abstract: Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks. Yet there is a need to lower gaze errors further to enable applications requiring higher quality. Further gains can be achieved by personalizing gaze networks, ideally with few calibration samples. However, over-parameterized neural networks are not amenable to learning from few examples as they can quickly over-fit. We embrace these challenges and propose a novel framework for Few-shot Adaptive GaZE Estimation (FAZE) for learning person-specific gaze networks with very few (less than or equal to 9) calibration samples. FAZE learns a rotation-aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator trained using meta-learning. It is capable of adapting to any new person to yield significant performance gains with as few as 3 samples, yielding state-of-the-art performance of 3.18 degrees on GazeCapture, a 19% improvement over prior art. We open-source our code at this https URL

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

Connected through hyperedge HE-2 (induced by source paper "a coarse to fine adaptive network for appearance based gaze estimation"):
  with members:
    - "light weight head pose invariant gaze tracking"
    - "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 "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"
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "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-4 (induced by source paper "content consistent generation of realistic eyes with style"):
  with members:
    - "photo realistic monocular gaze redirection using generative adversarial networks"
    - "openeds open eye dataset"
    - "semantic image synthesis with spatially adaptive normalization"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "demystifying neural style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-5 (induced by source paper "ritnet real time semantic segmentation of the eye for gaze tracking"):
  with members:
    - "eyenet a multi task network for off axis eye gaze estimation and user understanding"
    - "openeds open eye dataset"
    - "deep pictorial gaze estimation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "learning to personalize in appearance based gaze tracking"):
  with members:
    - "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"
    - "eye tracking for everyone"
    - "it s written all over your face full face appearance based gaze estimation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)