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: "rendering of eyes for eye shape registration and gaze estimation"
  Abstract: Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. We used our model's controllability to verify the importance of realistic illumination and shape variations in eye-region training data. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild.

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"
    - "appearance based 3d gaze estimation with personal calibration"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fully convolutional neural networks for raw eye tracking data segmentation generation and reconstruction"):
  with members:
    - "eye movement velocity and gaze data generator for evaluation robustness testing and assess of eye tracking software and visualization tools"
    - "pathgan visual scanpath prediction with generative adversarial networks"
    - "end to end eye movement detection using convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "appearance based 3d gaze estimation with personal calibration"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "purifying naturalistic images through a real time style transfer semantics network"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "guiding intelligent surveillance system by learning by synthesis gaze estimation"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "deep photo style transfer"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-6 (induced by source paper "mask guided style transfer network for purifying real images"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "guiding intelligent surveillance system by learning by synthesis gaze estimation"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "mask r cnn"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-7 (induced by source paper "openeds open eye dataset"):
  with members:
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "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-8 (induced by source paper "evaluation of appearance based methods and implications for gaze based applications"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "detecting low rapport during natural interactions in small groups from non verbal behaviour"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"
    - "tabletgaze unconstrained appearance based gaze estimation in mobile tablets"
    - "prediction of search targets from fixations in open world settings"

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)