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: "a vision based system for monitoring the loss of attention in automotive drivers"
  Abstract: Onboard monitoring of the alertness level of an automotive driver has been challenging to research in transportation safety and management. In this paper, we propose a robust real-time embedded platform to monitor the loss of attention of the driver during day and night driving conditions. The percentage of eye closure has been used to indicate the alertness level. In this approach, the face is detected using Haar-like features and is tracked using a Kalman filter. The eyes are detected using principal component analysis during daytime and using the block local-binary-pattern features during nighttime. Finally, the eye state is classified as open or closed using support vector machines. In-plane and off-plane rotations of the driver's face have been compensated using affine transformation and perspective transformation, respectively. Compensation in illumination variation is carried out using bihistogram equalization. The algorithm has been cross-validated using brain signals and, finally, has been implemented on a single-board computer that has an Intel Atom processor with a 1.66-GHz clock, a random access memory of 1 GB, ×86 architecture, and a Windows-embedded XP operating system. The system is found to be robust under actual driving conditions.

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"
    - "design and implementation of real time algorithms for eye tracking and perclos measurement for on board estimation of alertness of drivers"

Connected through hyperedge HE-2 (induced by source paper "a driver fatigue recognition algorithm based on spatio temporal feature sequence"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unitbox an advanced object detection network"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "compact convolutional neural network cascade for face detection"
    - "face alignment assisted by head pose estimation"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "network in network"

Connected through hyperedge HE-3 (induced by source paper "recognition of activities from eye gaze and egocentric video"):
  with members:
    - "a score level fusion method for eye movement biometrics"
    - "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-4 (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"
    - "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"
    - "appearance based gaze estimation in the wild"

Connected through hyperedge HE-5 (induced by source paper "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"):
  with members:
    - "appearance based gaze estimation in the wild"

Connected through hyperedge HE-6 (induced by source paper "specface a dataset of human faces wearing spectacles"):
  with members:
    - "automated alertness and emotion detection for empathic feedback during e learning"
    - "a video database of human faces under near infra red illumination for human computer interaction applications"

Connected through hyperedge HE-7 (induced by source paper "a drowsiness detection scheme based on fusion of voice and vision cues"):
  with members:
    - "automated alertness and emotion detection for empathic feedback during e learning"
    - "a video database of human faces under near infra red illumination for human computer interaction applications"

Connected through hyperedge HE-8 (induced by source paper "fast computation of perclos and saccadic ratio"):
  with members:
    - "a video database of human faces under near infra red illumination for human computer interaction applications"

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)