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 adaptive attention for joint facial action unit detection and face alignment"
  Abstract: Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.

Connected through hyperedge HE-1 (induced by source paper "region based adversarial synthesis of facial action units"):
  with members:
    - "conditional adversarial synthesis of 3d facial action units"
    - "deep face recognition a survey"
    - "attention gan for object transfiguration in wild images"
    - "ganimation anatomically aware facial animation from a single image"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "age progression regression by conditional adversarial autoencoder"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"

Connected through hyperedge HE-2 (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"
    - "self supervised learning of a facial attribute embedding from video"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "facial action unit detection using attention and relation learning"
    - "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-3 (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"
    - "facial action unit detection using attention and relation learning"
    - "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-4 (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"
    - "facial action unit detection using attention and relation learning"
    - "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-5 (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"
    - "facial action unit detection using attention and relation learning"
    - "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-6 (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"
    - "facial action unit detection using attention and relation learning"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-7 (induced by source paper "facial expression restoration based on improved graph convolutional networks"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "rain streak removal for single image via kernel guided cnn"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-8 (induced by source paper "explicit facial expression transfer via fine grained semantic representations"):
  with members:
    - "pluralistic image completion"
    - "spectral normalization for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "drit diverse image to image translation via disentangled representations"
    - "geometry guided adversarial facial expression synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

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)