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: "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
  Abstract: Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. Then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. The experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.

Connected through hyperedge HE-1 (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"
    - "facial action unit detection using attention and relation learning"
    - "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-2 (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"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "au r cnn encoding expert prior knowledge into r cnn for action unit detection"):
  with members:
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "multi view dynamic facial action unit detection"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "action unit detection with region adaptation multi labeling learning and optimal temporal fusing"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "facial landmark detection a literature survey"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "facial feature tracking under varying facial expressions and face poses based on restricted boltzmann machines"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "a hierarchical probabilistic model for facial feature detection"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "style aggregated network for facial landmark detection"):
  with members:
    - "exploring disentangled feature representation beyond face identification"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "gans for biological image synthesis"
    - "camera style adaptation for person re identification"
    - "pose invariant face alignment with a single cnn"
    - "face aging with conditional generative adversarial networks"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "anti makeup learning a bi level adversarial network for makeup invariant face verification"

Connected through hyperedge HE-6 (induced by source paper "wing loss for robust facial landmark localisation with convolutional neural networks"):
  with members:
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "dense face alignment"
    - "interspecies knowledge transfer for facial keypoint detection"

Connected through hyperedge HE-7 (induced by source paper "red net a recurrent encoder decoder network for video based face alignment"):
  with members:
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "track facial points in unconstrained videos"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "human pose estimation via convolutional part heatmap regression"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "facial action unit detection using attention and relation learning"):
  with members:
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

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)