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: "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
  Abstract: In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio features from videos. Spatial features are captured by convolutional neural networks, pretrained on large face recognition datasets. We show that usage of strong industry-level face recognition networks increases the accuracy of emotion recognition. Using our ensemble we improve on the previous best result on the test set by about 1 %, achieving a 60.03 % classification accuracy without any use of visual temporal information.

Connected through hyperedge HE-1 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning grimaces by watching tv"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

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"
    - "island loss for learning discriminative features in facial expression recognition"
    - "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 "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"
    - "island loss for learning discriminative features in facial expression recognition"
    - "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-4 (induced by source paper "emotion recognition in low resource settings an evaluation of automatic feature selection methods"):
  with members:
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "generalized fisher score for feature selection"

Connected through hyperedge HE-5 (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"
    - "covariance pooling for facial expression 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"

Connected through hyperedge HE-6 (induced by source paper "deep neural network augmentation generating faces for affect analysis"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "headon real time reenactment of human portrait videos"
    - "geometry contrastive gan for facial expression transfer"
    - "unsupervised training for 3d morphable model regression"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-7 (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"
    - "covariance pooling for facial expression recognition"
    - "ganimation anatomically aware facial animation from a single image"
    - "cake compact and accurate k dimensional representation of emotion"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-8 (induced by source paper "non volume preserving based feature fusion approach to group level expression recognition on crowd videos"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an attention model for group level emotion recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "group level emotion recognition using transfer learning from face identification"
    - "densely connected convolutional 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)