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: "modeling spatial and temporal cues for multi label facial action unit detection"
  Abstract: Facial action units (AUs) are essential to decode human facial expressions. Researchers have focused on training AU detectors with a variety of features and classifiers. However, several issues remain. These are spatial representation, temporal modeling, and AU correlation. Unlike most studies that tackle these issues separately, we propose a hybrid network architecture to jointly address them. Specifically, spatial representations are extracted by a Convolutional Neural Network (CNN), which, as analyzed in this paper, is able to reduce person-specific biases caused by hand-crafted features (eg, SIFT and Gabor). To model temporal dependencies, Long Short-Term Memory (LSTMs) are stacked on top of these representations, regardless of the lengths of input videos. The outputs of CNNs and LSTMs are further aggregated into a fusion network to produce per-frame predictions of 12 AUs. Our network naturally addresses the three issues, and leads to superior performance compared to existing methods that consider these issues independently. Extensive experiments were conducted on two large spontaneous datasets, GFT and BP4D, containing more than 400,000 frames coded with 12 AUs. On both datasets, we report significant improvement over a standard multi-label CNN and feature-based state-of-the-art. Finally, we provide visualization of the learned AU models, which, to our best knowledge, reveal how machines see facial AUs for the first time.

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"
    - "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"

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"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "social robots for people with developmental disabilities a user study on design features of a graphical user interface"
    - "i know how you feel emotion recognition with facial landmarks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "convolutional neural networks for facial expression recognition"
    - "facial emotion detection using convolutional neural networks and representational autoencoder units"
    - "going deeper in facial expression recognition using deep neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (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"
    - "deep structured learning for facial action unit intensity estimation"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

Connected through hyperedge HE-5 (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"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "semantic relationships guided representation learning for facial action unit recognition"):
  with members:
    - "deep structure inference network for facial action unit recognition"
    - "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"
    - "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-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "alzheimer s disease diagnosis based on cognitive methods in virtual environments and emotions analysis"):
  with members:
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "identifying stable patterns over time for emotion recognition from eeg"
    - "face expression recognition and analysis the state of the art"

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)