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: "poseidon face from depth for driver pose estimation"
  Abstract: Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth approach for learning image faces from depth. Results in face reconstruction are qualitatively impressive. 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. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.

Connected through hyperedge HE-1 (induced by source paper "attention aware fusion rgb d face recognition"):
  with members:
    - "classification of hand movements from eeg using a deep attention based lstm network"
    - "face recognition a novel multi level taxonomy based survey"
    - "capsule attention for multimodal eeg and eog spatiotemporal representation learning with application to driver vigilance estimation"
    - "robust rgb d face recognition using attribute aware loss"
    - "deep face recognition a survey"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-2 (induced by source paper "exploiting temporal consistency for real time video depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "rethinking monocular depth estimation with adversarial training"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepvs a deep learning based video saliency prediction approach"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-3 (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"
    - "face from depth for head pose estimation on depth images"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-4 (induced by source paper "learning to generate facial depth maps"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "learning representations for automatic colorization"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"

Connected through hyperedge HE-5 (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"
    - "driver distraction detection and recognition using rgb d sensor"

Connected through hyperedge HE-6 (induced by source paper "subjective annotations for vision based attention level estimation"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"

Connected through hyperedge HE-7 (induced by source paper "face from depth for head pose estimation on depth images"):
  with members:
    - "improving heterogeneous face recognition with conditional adversarial networks"
    - "faceness net face detection through deep facial part responses"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "coupled generative adversarial networks"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-8 (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"
    - "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"
    - "what do different evaluation metrics tell us about saliency models"

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)