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: "from depth data to head pose estimation a siamese approach"
  Abstract: The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss function to improve the learning of the regression network layer. The system has been tested on two public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported results demonstrate the improvement in accuracy with respect to current state-of-the-art approaches and the real time capabilities of the overall framework.

Connected through hyperedge HE-1 (induced by source paper "a differential approach for gaze estimation"):
  with members:
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "efficient eye typing with 9 direction gaze estimation"
    - "learning to compare relation network for few shot learning"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "3d gaze estimation from 2d pupil positions on monocular head mounted eye trackers"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "improving head pose estimation with a combined loss and bounding box margin adjustment"):
  with members:
    - "fine grained head pose estimation without keypoints"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "an all in one convolutional neural network for face analysis"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "single image based head pose estimation with spherical parameterization and 3d morphing"):
  with members:
    - "searching for mobilenetv3"
    - "fine grained head pose estimation without keypoints"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"

Connected through hyperedge HE-5 (induced by source paper "3d human pose estimation with siamese equivariant embedding"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "exploiting temporal information for 3d human pose estimation"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (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"
    - "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"
    - "driver distraction detection and recognition using rgb d sensor"

Connected through hyperedge HE-7 (induced by source paper "head detection with depth images in the wild"):
  with members:
    - "deep head pose estimation from depth data for in car automotive applications"
    - "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)