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: "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
  Abstract: We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people of different ethnicities and a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, and make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution and vision aids as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.

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 "relative afferent pupillary defect screening through transfer learning"):
  with members:
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "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"

Connected through hyperedge HE-3 (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-4 (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-5 (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"
    - "a survey of semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "image based eye gaze tracking and its applications"):
  with members:
    - "escaf pupil centre localization algorithm with candidate filtering"
    - "recognition of activities from eye gaze and egocentric video"
    - "a framework for fast face and eye detection"
    - "else ellipse selection for robust pupil detection in real world environments"
    - "a drowsiness detection scheme based on fusion of voice and vision cues"
    - "appearance based gaze estimation in the wild"
    - "design and implementation of real time algorithms for eye tracking and perclos measurement for on board estimation of alertness of drivers"
    - "an improved algorithm for eye corner detection"

Connected through hyperedge HE-7 (induced by source paper "guiding intelligent surveillance system by learning by synthesis gaze estimation"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "learning to predict crisp boundaries"
    - "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"

Connected through hyperedge HE-8 (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"
    - "pupilnet convolutional neural networks for robust pupil detection"
    - "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)