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 structured learning for facial action unit intensity estimation"
  Abstract: We consider the task of automated estimation of facial expression intensity. This involves estimation of multiple output variables (facial action units --- AUs) that are structurally dependent. Their structure arises from statistically induced co-occurrence patterns of AU intensity levels. Modeling this structure is critical for improving the estimation performance; however, this performance is bounded by the quality of the input features extracted from face images. The goal of this paper is to model these structures and estimate complex feature representations simultaneously by combining conditional random field (CRF) encoded AU dependencies with deep learning. To this end, we propose a novel Copula CNN deep learning approach for modeling multivariate ordinal variables. Our model accounts for $ordinal$ structure in output variables and their $non$-$linear$ dependencies via copula functions modeled as cliques of a CRF. These are jointly optimized with deep CNN feature encoding layers using a newly introduced balanced batch iterative training algorithm. We demonstrate the effectiveness of our approach on the task of AU intensity estimation on two benchmark datasets. We show that joint learning of the deep features and the target output structure results in significant performance gains compared to existing deep structured models for analysis of facial expressions.

Connected through hyperedge HE-1 (induced by source paper "inferring dynamic representations of facial actions from a still image"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a functional regression approach to facial landmark tracking"
    - "action anticipation by predicting future dynamic images"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"
    - "rank pooling for action recognition"

Connected through hyperedge HE-2 (induced by source paper "feafa a well annotated dataset for facial expression analysis and 3d facial animation"):
  with members:
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition with 4kresolution database"):
  with members:
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "a deep learning perspective on the origin of facial expressions"

Connected through hyperedge HE-4 (induced by source paper "reenactgan learning to reenact faces via boundary transfer"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "densepose dense human pose estimation in the wild"
    - "quantifying facial age by posterior of age comparisons"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep structure inference network for facial action unit recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "automatic recognition of facial displays of unfelt emotions"
    - "action unit detection with region adaptation multi labeling learning and optimal temporal fusing"
    - "structured feature learning for pose estimation"
    - "crf cnn modeling structured information in human pose estimation"
    - "conditional random fields as recurrent neural networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"

Connected through hyperedge HE-7 (induced by source paper "automated pain detection from facial expressions using facs a review"):
  with members:
    - "deepfacelift interpretable personalized models for automatic estimation of self reported pain"
    - "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
    - "learning pain from action unit combinations a weakly supervised approach via multiple instance learning"
    - "lomo latent ordinal model for facial analysis in videos"
    - "machine based multimodal pain assessment tool for infants a review"
    - "very deep convolutional networks for large scale image recognition"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "deepcoder semi parametric variational autoencoders for automatic facial action coding"):
  with members:
    - "conditional image generation with pixelcnn decoders"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "generating sentences from a continuous space"
    - "variational auto encoded deep gaussian processes"
    - "deep convolutional inverse graphics network"
    - "draw a recurrent neural network for image generation"
    - "autoencoding beyond pixels using a learned similarity metric"

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)