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: "face from depth for head pose estimation on depth images"
  Abstract: Depth cameras allow to setup reliable solutions for people monitoring and behavior understanding, specially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only. A head detection and localization module is also included, in order to develop a complete end-to-end system. The core element of the framework is a Convolutional Neural Network, called POSEidon+, that receives as input three types of images and provides the 3D angles of the pose as output. Moreover, a Face-from-Depth component based on a Deterministic Conditional GAN model is able to hallucinate a face from the corresponding depth image and we empirically demonstrate that this positively impacts the system performances. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Experimental results show that our method overcomes all recent state-of-art works based on both intensity and depth input data, running in real time at more than 30 frames per second.

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"
    - "deep head pose estimation from depth data for in car automotive applications"
    - "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"
    - "deep head pose estimation from depth data for in car automotive applications"
    - "poseidon face from depth for driver pose estimation"
    - "driver distraction detection and recognition using rgb d sensor"

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)