human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pure robust pupil detection for real time pervasive eye tracking"):
  Abstract: Abstract   Real-time, accurate, and robust pupil detection is an essential prerequisite to enable pervasive eye-tracking and its applications – e.g., gaze-based human computer interaction, health monitoring, foveated rendering, and advanced driver assistance. However, automated pupil detection has proved to be an intricate task in real-world scenarios due to a large mixture of challenges such as quickly changing illumination and occlusions. In this paper, we introduce the   Pu pil  Re constructor  ( PuRe ), a method for pupil detection in pervasive scenarios based on a novel edge segment selection and conditional segment combination schemes; the method also includes a confidence measure for the detected pupil. The proposed method was evaluated on over 316,000 images acquired with four distinct head-mounted eye tracking devices. Results show a pupil detection rate improvement of over 10 percentage points w.r.t. state-of-the-art algorithms in the two most challenging data sets (6.46 for all data sets), further pushing the envelope for pupil detection. Moreover, we advance the evaluation protocol of pupil detection algorithms by also considering eye images in which pupils are not present and contributing a new data set of mostly closed eyes images. In this aspect,  PuRe  improved  precision  and  specificity  w.r.t. state-of-the-art algorithms by 25.05 and 10.94 percentage points, respectively, demonstrating the meaningfulness of  PuRe ’s confidence measure.  PuRe  operates in real-time for modern eye trackers (at 120  fps ) and is fully integrated into  EyeRecToo  – an open-source state-of-the-art software for pervasive head-mounted eye tracking. The proposed method and data set are available at  http://www.ti.uni-tuebingen.de/perception .

Includes members:
  Member "a novel performance evaluation methodology for single target trackers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "visual object tracking performance measures revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic discovery of political meme genres with diverse appearances")
  Member "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality")
  Member "pupilnet convolutional neural networks for robust pupil detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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")
  Member "else ellipse selection for robust pupil detection in real world environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relative afferent pupillary defect screening through transfer learning")
  Member "bayesian identification of fixations saccades and smooth pursuits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gaze in wild a dataset for studying eye and head coordination in everyday activities")
  Member "cudnn efficient primitives for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching for winograd aware quantized networks")

Question: Please tell me which class the source paper 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)