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: "covariance pooling for facial expression recognition"
  Abstract: Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial fea- tures. In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition. In particular, we first employ such kind of manifold networks in conjunction with tradi- tional convolutional networks for spatial pooling within in- dividual image feature maps in an end-to-end deep learning manner. By doing so, we are able to achieve a recognition accuracy of 58.14% on the validation set of Static Facial Expressions in the Wild (SFEW 2.0) and 87.0% on the vali- dation set of Real-World Affective Faces (RAF) Database. Both of these results are the best results we are aware of. Besides, we leverage covariance pooling to capture the tem- poral evolution of per-frame features for video-based facial expression recognition. Our reported results demonstrate the advantage of pooling image-set features temporally by stacking the designed manifold network of covariance pool-ing on top of convolutional network layers.

Connected through hyperedge HE-1 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "frame attention networks for facial expression recognition in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-2 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "task2vec task embedding for meta learning"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-3 (induced by source paper "few shot learning with localization in realistic settings"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "low shot learning from imaginary data"
    - "power normalizing second order similarity network for few shot learning"
    - "prototypical networks for few shot learning"
    - "extreme clicking for efficient object annotation"
    - "a simple neural attentive meta learner"
    - "residual attention network for image classification"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-4 (induced by source paper "deep facial expression recognition a survey"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep covariance descriptors for facial expression recognition"
    - "geometry contrastive gan for facial expression transfer"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"
    - "relaxed spatio temporal deep feature aggregation for real fake expression prediction"

Connected through hyperedge HE-5 (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "island loss for learning discriminative features in facial expression recognition"
    - "automatic recognition of facial displays of unfelt emotions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-6 (induced by source paper "the many moods of emotion"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep video portraits"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "ganimation anatomically aware facial animation from a single image"
    - "cake compact and accurate k dimensional representation of emotion"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-7 (induced by source paper "an occam s razor view on learning audiovisual emotion recognition with small training sets"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "learnable pooling with context gating for video classification"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "improved regularization of convolutional neural networks with cutout"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "lglg wpca an effective texture based method for face recognition"):
  with members:
    - "multi fold gabor pca and ica filter convolution descriptor for face recognition"
    - "learning a high fidelity pose invariant model for high resolution face frontalization"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "squeeze and excitation networks"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "kernel methods on approximate infinite dimensional covariance operators for image classification"

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)