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: "island loss for learning discriminative features in facial expression recognition"
  Abstract: Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. #R##N#In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.

Connected through hyperedge HE-1 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "frame attention networks for facial expression recognition in videos"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "deep facial expression recognition a survey"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention is all you need"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-2 (induced by source paper "frame attention networks for facial expression recognition in videos"):
  with members:
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "neural aggregation network for video face recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "lomo latent ordinal model for facial analysis in videos"

Connected through hyperedge HE-3 (induced by source paper "facial expression recognition using human to animated character expression translation"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "facial expression recognition using disentangled adversarial learning"
    - "deep facial expression recognition a survey"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "context encoders feature learning by inpainting"
    - "convolutional experts constrained local model for facial landmark detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "identity free facial expression recognition using conditional generative adversarial network"):
  with members:
    - "deep facial expression recognition a survey"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "image to image translation with conditional adversarial networks"
    - "lomo latent ordinal model for facial analysis in videos"
    - "peak piloted deep network for facial expression recognition"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "deep texture manifold for ground terrain recognition"
    - "probabilistic attribute tree in convolutional neural networks for facial expression recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-6 (induced by source paper "facial expression recognition using disentangled adversarial learning"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "deep facial expression recognition a survey"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "convolutional experts constrained local model for facial landmark detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "lbvcnn local binary volume convolutional neural network for facial expression recognition from image sequences"):
  with members:
    - "going deeper in facial expression recognition using deep neural networks"
    - "lomo latent ordinal model for facial analysis in videos"
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines"

Connected through hyperedge HE-8 (induced by source paper "facial expression representation learning by synthesizing expression images"):
  with members:
    - "facial expression recognition using human to animated character expression translation"
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "peak piloted deep network for facial expression recognition"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)