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: "facial action unit detection using attention and relation learning"
  Abstract: Attention mechanism has recently attracted increasing attentions in the area of facial action unit (AU) detection. By finding the region of interest (ROI) of each AU with the attention mechanism, AU related local features can be captured. Most existing attention based AU detection works use prior knowledge to generate fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose a novel end-to-end weakly-supervised attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise attentions and spatial attentions are learned to select and extract AU related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework (i) outperforms the state-of-the-art methods for AU detection, and (ii) can find the ROI of each AU and capture the relations among AUs adaptively.

Connected through hyperedge HE-1 (induced by source paper "semi supervised unconstrained action unit detection via latent feature domain"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "self supervised learning of a facial attribute embedding from video"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "residual parameter transfer for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-2 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal relation and attention learning for facial action unit detection"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "deep structure inference network for facial action unit recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "areas of attention for image captioning"
    - "recurrent attentional networks for saliency detection"

Connected through hyperedge HE-4 (induced by source paper "geoconv geodesic guided convolution for facial action unit recognition"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "conditional adversarial synthesis of 3d facial action units"
    - "deep structure inference network for facial action unit recognition"
    - "depth aware cnn for rgb d segmentation"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "deformable convolutional networks"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-5 (induced by source paper "upper middle and lower region learning for facial action unit detection"):
  with members:
    - "spatio temporal relation and attention learning for facial action unit detection"
    - "feratt facial expression recognition with attention net"
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "facial expression recognition using disentangled adversarial learning"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-6 (induced by source paper "facial motion prior networks for facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "deep generative contrastive networks for facial expression recognition"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "classifying and visualizing emotions with emotional dan"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "i know how you feel emotion recognition with facial landmarks"
    - "the indian spontaneous expression database for emotion recognition"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "improving landmark localization with semi supervised learning"

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)