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: "spatio temporal facial expression recognition using convolutional neural networks and conditional random fields"
  Abstract: Automated Facial Expression Recognition (FER) has been a challenging task for decades. Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition. These methods often require rigorous hyperparameter tuning to achieve good results. Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition. In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos. The first part captures the spatial relation within facial images using convolutional layers followed by three Inception-ResNet modules and two fully-connected layers. To capture the temporal relation between the image frames, we use linear chain CRF in the second part of our network. We evaluate our proposed network on three publicly available databases, viz. CK+, MMI, and FERA. Experiments are performed in subject-independent and cross-database manners. Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the state-of-the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.

Connected through hyperedge HE-1 (induced by source paper "a deeper look at facial expression dataset bias"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "expertnet exigent features preservative network for facial expression recognition"):
  with members:
    - "deep generative contrastive networks for facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "deep residual learning for image recognition"
    - "dexpression deep convolutional neural network for expression recognition"
    - "deep temporal appearance geometry network for facial expression recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"):
  with members:
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "off apexnet on micro expression recognition system"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "a review on facial micro expressions analysis datasets features and metrics"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "facial expression recognition using enhanced deep 3d convolutional neural networks"):
  with members:
    - "facial expression recognition from world wild web"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "facial affect estimation in the wild using deep residual and convolutional networks"):
  with members:
    - "facial expression recognition from world wild web"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "going deeper in facial expression recognition using deep neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"

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)