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: "lomo latent ordinal model for facial analysis in videos"
  Abstract: We study the problem of facial analysis in videos. We propose a novel weakly supervised learning method that models the video event (expression, pain etc.) as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for smile, brow lower and cheek raise for pain). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations. In combination with complimentary features, we report state-of-the-art results on these datasets.

Connected through hyperedge HE-1 (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"
    - "island loss for learning discriminative features in facial expression recognition"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "identity free facial expression recognition using conditional generative adversarial network"):
  with members:
    - "deep facial expression recognition a survey"
    - "island loss for learning discriminative features in facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "image to image translation with conditional adversarial networks"
    - "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-3 (induced by source paper "lbvcnn local binary volume convolutional neural network for facial expression recognition from image sequences"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "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-4 (induced by source paper "probabilistic attribute tree in convolutional neural networks for facial expression recognition"):
  with members:
    - "multi region ensemble convolutional neural network for facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"
    - "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"
    - "an all in one convolutional neural network for face analysis"
    - "joint unsupervised learning of deep representations and image clusters"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "automated pain detection from facial expressions using facs a review"):
  with members:
    - "deep structured learning for facial action unit intensity estimation"
    - "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"
    - "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-6 (induced by source paper "mode variational lstm robust to unseen modes of variation application to facial expression recognition"):
  with members:
    - "learning expressionlets via universal manifold model for dynamic facial expression recognition"
    - "robust face recognition via multimodal deep face representation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "microexpnet an extremely small and fast model for expression recognition from frontal face images"):
  with members:
    - "deep generative contrastive networks for facial expression recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "from facial expression recognition to interpersonal relation prediction"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "island loss for learning discriminative features in facial expression recognition"):
  with members:
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "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"
    - "differential angular imaging for material recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning using linear support vector machines"

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)