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: "owl and lizard patterns of head pose and eye pose in driver gaze classification"
  Abstract: Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.

Connected through hyperedge HE-1 (induced by source paper "mit advanced vehicle technology study large scale naturalistic driving study of driver behavior and interaction with automation"):
  with members:
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "automated synchronization of driving data using vibration and steering events"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"
    - "what can be predicted from six seconds of driver glances"
    - "pyramid scene parsing network"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-2 (induced by source paper "single image based head pose estimation with spherical parameterization and 3d morphing"):
  with members:
    - "searching for mobilenetv3"
    - "fine grained head pose estimation without keypoints"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "from depth data to head pose estimation a siamese approach"

Connected through hyperedge HE-3 (induced by source paper "looking at the driver rider in autonomous vehicles to predict take over readiness"):
  with members:
    - "forced spatial attention for driver foot activity classification"
    - "attention monitoring and hazard assessment with bio sensing and vision empirical analysis utilizing cnns on the kitti dataset"
    - "handynet a one stop solution to detect segment localize analyze driver hands"
    - "driver gaze zone estimation using convolutional neural networks a general framework and ablative analysis"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "an intelligent safety system for human centered semi autonomous vehicles"):
  with members:
    - "towards end to end lane detection an instance segmentation approach"
    - "efficient road lane marking detection with deep learning"
    - "human centered autonomous vehicle systems principles of effective shared autonomy"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "imitation learning for vision based lane keeping assistance"
    - "end to end learning for self driving cars"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "driver gaze zone estimation using convolutional neural networks a general framework and ablative analysis"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep residual learning for image recognition"
    - "driver gaze region estimation without using eye movement"
    - "learning deep features for discriminative localization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "dynamics of driver s gaze explorations in behavior modeling maneuver prediction"):
  with members:
    - "learning where to attend like a human driver"
    - "what can be predicted from six seconds of driver glances"
    - "it s written all over your face full face appearance based gaze estimation"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"

Connected through hyperedge HE-7 (induced by source paper "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"):
  with members:
    - "eye tracking for everyone"
    - "real time eye gaze direction classification using convolutional neural network"
    - "facevr real time facial reenactment and eye gaze control in virtual reality"
    - "appearance based gaze estimation in the wild"

Connected through hyperedge HE-8 (induced by source paper "what can be predicted from six seconds of driver glances"):
  with members:
    - "deep learning in neural networks"
    - "smote synthetic minority over sampling technique"

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)