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: "driver gaze zone estimation using convolutional neural networks a general framework and ablative analysis"
  Abstract: Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful for determining the handoff time to a human driver. While there has been significant improvement in personalized driver gaze zone estimation systems, a generalized system which is invariant to different subjects, perspectives and scales is still lacking. We take a step towards this generalized system using Convolutional Neural Networks (CNNs). We finetune 4 popular CNN architectures for this task, and provide extensive comparisons of their outputs. We additionally experiment with different input image patches, and also examine how image size affects performance. For training and testing the networks, we collect a large naturalistic driving dataset comprising of 11 long drives, driven by 10 subjects in two different cars. Our best performing model achieves an accuracy of 95.18% during cross-subject testing, outperforming current state of the art techniques for this task. Finally, we evaluate our best performing model on the publicly available Columbia Gaze Dataset comprising of images from 56 subjects with varying head pose and gaze directions. Without any training, our model successfully encodes the different gaze directions on this diverse dataset, demonstrating good generalization capabilities.

Connected through hyperedge HE-1 (induced by source paper "driver gaze estimation in the real world overcoming the eyeglass challenge"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "unsupervised eyeglasses removal in the wild"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-2 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"

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"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "adam a method for stochastic optimization"

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)