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: "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"
  Abstract: Iris centre (IC) localisation in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks etc. This study proposes an efficient method for determining IC in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for IC localisation. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution-based approach is used for obtaining the coarse location of IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases such as BioID, Gi4E and is found to outperform the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "efficient cnn implementation for eye gaze estimation on low power low quality consumer imaging systems"):
  with members:
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "recognition of activities from eye gaze and egocentric video"):
  with members:
    - "a score level fusion method for eye movement biometrics"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "a vision based system for monitoring the loss of attention in automotive drivers"

Connected through hyperedge HE-3 (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"
    - "else ellipse selection for robust pupil detection in real world environments"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-4 (induced by source paper "hybrid eye center localization using cascaded regression and hand crafted model fitting"):
  with members:
    - "else ellipse selection for robust pupil detection in real world environments"
    - "appearance based gaze estimation in the wild"

Connected through hyperedge HE-5 (induced by source paper "real time eye gaze direction classification using convolutional neural network"):
  with members:
    - "a score level fusion method for eye movement biometrics"
    - "appearance based gaze estimation in the wild"
    - "a vision based system for monitoring the loss of attention in automotive drivers"

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)