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: "design and implementation of real time algorithms for eye tracking and perclos measurement for on board estimation of alertness of drivers"
  Abstract: The alertness level of drivers can be estimated with the use of computer vision based methods. The level of fatigue can be found from the value of PERCLOS. It is the ratio of closed eye frames to the total frames processed. The main objective of the thesis is the design and implementation of real-time algorithms for measurement of PERCLOS. In this work we have developed a real-time system which is able to process the video onboard and to alarm the driver in case the driver is in alert. For accurate estimation of PERCLOS the frame rate should be greater than 4 and accuracy should be greater than 90%. For eye detection we have used mainly two approaches Haar classifier based method and Principal Component Analysis (PCA) based method for day time. During night time active Near Infra Red (NIR) illumination is used. Local Binary Pattern (LBP) histogram based method is used for the detection of eyes at night time. The accuracy rate of the algorithms was found to be more than 90% at frame rates more than 5 fps which was suitable for the application.

Connected through hyperedge HE-1 (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"
    - "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"
    - "an improved algorithm for eye corner detection"

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)