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 distraction detection and recognition using rgb d sensor"
  Abstract: Abstract—Driver inattention assessment has become a veryactive ﬁeld in intelligent transportation systems. Based on activesensor Kinect and computer vision tools, we have built an efﬁcientmodule for detecting driver distraction and recognizing the typeof distraction. Based on color and depth map data from theKinect, our system is composed of four sub-modules. We callthem eye behavior (detecting gaze and blinking), arm position (isthe right arm up, down, right of forward), head orientation, andfacial expressions. Each module produces relevant informationfor assessing driver inattention. They are merged together lateron using two different classiﬁcation strategies: AdaBoost classiﬁerand Hidden Markov Model. Evaluation is done using a drivingsimulator and 8 drivers of different gender, age and nationalityfor a total of more than 8 hours of recording. Qualitative andquantitative results show strong and accurate detection andrecognition capacity (85% accuracy for the type of distractionand 90% for distraction detection). Moreover, each module isobtained independently and could be used for other types ofinference, such as fatigue detection, and could be implementedfor real cars systems.

Connected through hyperedge HE-1 (induced by source paper "driver distraction identification with an ensemble of convolutional neural networks"):
  with members:
    - "static free space detection with laser scanner using occupancy grid maps"
    - "learning transferable architectures for scalable image recognition"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "reactive collision avoidance using evolutionary neural networks"
    - "multi view face detection using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "combining deep and depth deep learning and face depth maps for driver attention monitoring"):
  with members:
    - "domain translation with conditional gans from depth to rgb face to face"
    - "learning to generate facial depth maps"
    - "from depth data to head pose estimation a siamese approach"
    - "head detection with depth images in the wild"
    - "deep head pose estimation from depth data for in car automotive applications"
    - "face from depth for head pose estimation on depth images"
    - "poseidon face from depth for driver pose estimation"

Connected through hyperedge HE-3 (induced by source paper "real time distracted driver posture classification"):
  with members:
    - "multi view face detection using deep convolutional neural networks"
    - "rethinking the inception architecture for computer vision"
    - "a pattern recognition system for detecting use of mobile phones while driving"

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)