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 gaze estimation in the wild"
  Abstract: Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.

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

Connected through hyperedge HE-4 (induced by source paper "a neural architecture for detecting confusion in eye tracking data"):
  with members:
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "imagenet large scale visual recognition challenge"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "are you really looking at me a feature extraction framework for estimating interpersonal eye gaze from conventional video"):
  with members:
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
    - "real time eye gaze direction classification using convolutional neural network"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-6 (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"
    - "light weight head pose invariant gaze tracking"
    - "deep pictorial gaze estimation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"

Connected through hyperedge HE-7 (induced by source paper "photo realistic monocular gaze redirection using generative adversarial networks"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "multimodal unsupervised image to image translation"
    - "deep pictorial gaze estimation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (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"
    - "light weight head pose invariant gaze tracking"
    - "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"

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)