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: "deep head pose estimation from depth data for in car automotive applications"
  Abstract: Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements.

Connected through hyperedge HE-1 (induced by source paper "domain translation with conditional gans from depth to rgb face to face"):
  with members:
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"
    - "from depth data to head pose estimation a siamese approach"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image restoration using autoencoding priors"
    - "face from depth for head pose estimation on depth images"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "poseidon face from depth for driver pose estimation"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

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"
    - "face from depth for head pose estimation on depth images"
    - "poseidon face from depth for driver pose estimation"
    - "driver distraction detection and recognition using rgb d sensor"

Connected through hyperedge HE-3 (induced by source paper "learning to map vehicles into bird s eye view"):
  with members:
    - "ssd single shot multibox detector"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "playing for data ground truth from computer games"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "deep residual learning for image recognition"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "head detection with depth images in the wild"):
  with members:
    - "from depth data to head pose estimation a siamese approach"
    - "poseidon face from depth for driver pose estimation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "context aware cnns for person head detection"
    - "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)