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: "pupilnet convolutional neural networks for robust pupil detection"
  Abstract: Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking. However, automated pupil detection in real-world scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non centered and off-axis eye recording, and physiological eye characteristics. In this paper, we propose and evaluate a method based on a novel dual convolutional neural network pipeline. In its first stage the pipeline performs coarse pupil position identification using a convolutional neural network and subregions from a downscaled input image to decrease computational costs. Using subregions derived from a small window around the initial pupil position estimate, the second pipeline stage employs another convolutional neural network to refine this position, resulting in an increased pupil detection rate up to 25% in comparison with the best performing state-of-the-art algorithm. Annotated data sets can be made available upon request.

Connected through hyperedge HE-1 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression"):
  with members:
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a survey on periocular biometrics research"
    - "iris recognition after death"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "human iris recognition in post mortem subjects study and database"

Connected through hyperedge HE-2 (induced by source paper "relative afferent pupillary defect screening through transfer learning"):
  with members:
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "else ellipse selection for robust pupil detection in real world environments"
    - "least squares fitting of circles and lines"

Connected through hyperedge HE-3 (induced by source paper "eyenet attention based convolutional encoder decoder network for eye region segmentation"):
  with members:
    - "openeds open eye dataset"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "pure robust pupil detection for real time pervasive eye tracking"):
  with members:
    - "a novel performance evaluation methodology for single target trackers"
    - "visual object tracking performance measures revisited"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "else ellipse selection for robust pupil detection in real world environments"
    - "bayesian identification of fixations saccades and smooth pursuits"
    - "cudnn efficient primitives for deep learning"

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)