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 v2 0 convolutional neural networks for cpu based real time 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 realworld scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non-centered and off-axis eye recording, as well as physiological eye characteristics. In this paper, we approach this challenge through: I) a convolutional neural network (CNN) running in real time on a single core, II) a novel computational intensive two stage CNN for accuracy improvement, and III) a fast propability distribution based refinement method as a practical alternative to II. We evaluate the proposed approaches against the state-of-the-art pupil detection algorithms, improving the detection rate up to ~9% percent points on average over all data sets (~7% on one CPU core 7ms). This evaluation was performed on over 135,000 images: 94,000 images from the literature, and 41,000 new hand-labeled and challenging images contributed by this work (v1.0).

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"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "human iris recognition in post mortem subjects study and database"
    - "3d gaze estimation from 2d pupil positions on monocular head mounted eye trackers"

Connected through hyperedge HE-2 (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"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "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-3 (induced by source paper "relative afferent pupillary defect screening through transfer learning"):
  with members:
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "pupilnet convolutional neural networks for robust pupil detection"
    - "else ellipse selection for robust pupil detection in real world environments"
    - "least squares fitting of circles and lines"

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)