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 structure inference network for facial action unit recognition"
  Abstract: Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for developing general facial expression analysis. In recent years, most efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between Action Units. In this paper, we propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively.

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 adaptive attention for joint facial action unit detection and face alignment"
    - "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-2 (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"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "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-3 (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"
    - "depth aware cnn for rgb d segmentation"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "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-4 (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 adaptive attention for joint facial action unit detection and face alignment"
    - "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-5 (induced by source paper "semantic relationships guided representation learning for facial action unit recognition"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "instance level salient object segmentation"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "gated graph sequence neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "deep residual learning for image recognition"
    - "a century of portraits a visual historical record of american high school yearbooks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "multi label co regularization for semi supervised facial action unit recognition"):
  with members:
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "deep co training for semi supervised image recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "empirical evaluation of rectified activations in convolutional network"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-7 (induced by source paper "on the effect of age perception biases for real age regression"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "attended end to end architecture for age estimation from facial expression videos"
    - "apparent age estimation using ensemble of deep learning models"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "a scalable approach for facial action unit classifier training using noisy data for pre training"):
  with members:
    - "reproducibility in machine learning for health"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "all you need is a good init"
    - "do deep neural networks learn facial action units when doing expression recognition"

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)