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: "openeds open eye dataset"
  Abstract: We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. A baseline experiment has been evaluated on OpenEDS for the task of semantic segmentation of pupil, iris, sclera and background, with the mean intersectionover-union (mIoU) of 98.3 %. We anticipate that OpenEDS will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications. The dataset is available for download upon request at this https URL

Connected through hyperedge HE-1 (induced by source paper "content consistent generation of realistic eyes with style"):
  with members:
    - "photo realistic monocular gaze redirection using generative adversarial networks"
    - "few shot adaptive gaze estimation"
    - "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-2 (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"
    - "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"
    - "learning to personalize in appearance based gaze tracking"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "eyenet attention based convolutional encoder decoder network for eye region segmentation"):
  with members:
    - "skeletonnet shape pixel to skeleton pixel"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "cbam convolutional block attention module"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "eye tracking for everyone"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "shape constrained network for eye segmentation in the wild"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "generative adversarial networks an overview"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "ocular recognition databases and competitions a survey"):
  with members:
    - "deep representations for cross spectral ocular biometrics"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a survey on periocular biometrics research"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-6 (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"
    - "few shot adaptive gaze estimation"
    - "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-7 (induced by source paper "eye semantic segmentation with a lightweight model"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "averaging weights leads to wider optima and better generalization"
    - "squeeze and excitation networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)