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: "else ellipse selection for robust pupil detection in real world environments"
  Abstract: Fast and robust pupil detection is an essential prerequisite for video-based eye-tracking in real-world settings. Several algorithms for image-based pupil detection have been proposed, their applicability is mostly limited to laboratory conditions. In realworld scenarios, automated pupil detection has to face various challenges, such as illumination changes, reflections (on glasses), make-up, non-centered eye recording, and physiological eye characteristics. We propose ElSe, a novel algorithm based on ellipse evaluation of a filtered edge image. We aim at a robust, resource-saving approach that can be integrated in embedded architectures e.g. driving. The proposed algorithm was evaluated against four state-of-the-art methods on over 93,000 hand-labeled images from which 55,000 are new images contributed by this work. On average, the proposed method achieved a 14.53% improvement on the detection rate relative to the best state-of-the-art performer. download:this ftp URL de (password:eyedata).

Connected through hyperedge HE-1 (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"
    - "pupilnet convolutional neural networks for robust pupil detection"
    - "least squares fitting of circles and lines"

Connected through hyperedge HE-2 (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"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "a framework for fast face and eye detection"
    - "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-3 (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"
    - "pupilnet convolutional neural networks for robust pupil detection"
    - "bayesian identification of fixations saccades and smooth pursuits"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "escaf pupil centre localization algorithm with candidate filtering"):
  with members:
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-5 (induced by source paper "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "caffe convolutional architecture for fast feature embedding"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"
    - "fully convolutional networks for semantic segmentation"
    - "recurrent convolutional neural networks for scene parsing"

Connected through hyperedge HE-6 (induced by source paper "hybrid eye center localization using cascaded regression and hand crafted model fitting"):
  with members:
    - "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"
    - "appearance based gaze estimation in the wild"

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)