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 region estimation without using eye movement"
  Abstract: Automated estimation of the allocation of a driver's visual attention may be a critical component of future Advanced Driver Assistance Systems. In theory, vision-based tracking of the eye can provide a good estimate of gaze location. In practice, eye tracking from video is challenging because of sunglasses, eyeglass reflections, lighting conditions, occlusions, motion blur, and other factors. Estimation of head pose, on the other hand, is robust to many of these effects, but cannot provide as fine-grained of a resolution in localizing the gaze. However, for the purpose of keeping the driver safe, it is sufficient to partition gaze into regions. In this effort, we propose a system that extracts facial features and classifies their spatial configuration into six regions in real-time. Our proposed method achieves an average accuracy of 91.4% at an average decision rate of 11 Hz on a dataset of 50 drivers from an on-road study.

Connected through hyperedge HE-1 (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"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"
    - "deep residual learning for image recognition"
    - "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-2 (induced by source paper "predicting the driver s focus of attention the dr eye ve project"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "understanding convolution for semantic segmentation"
    - "poseidon face from depth for driver pose estimation"
    - "learning where to attend like a human driver"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a deep multi level network for saliency prediction"
    - "3d bounding box estimation using deep learning and geometry"
    - "semantic video segmentation by gated recurrent flow propagation"

Connected through hyperedge HE-3 (induced by source paper "learning where to attend like a human driver"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a statistical test for joint distributions equivalence"
    - "deep transfer learning with joint adaptation networks"
    - "what do different evaluation metrics tell us about saliency models"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "learning spatiotemporal features with 3d convolutional networks"

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)