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: "facial expression recognition from world wild web"
  Abstract: Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%.

Connected through hyperedge HE-1 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "improving person re identification by attribute and identity learning"
    - "deep facial expression recognition a survey"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "low shot learning from imaginary data"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"

Connected through hyperedge HE-2 (induced by source paper "recognizing facial expressions of occluded faces using convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "improving bag of visual words towards effective facial expressive image classification"
    - "facial expression recognition using a hybrid cnn sift aggregator"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "eyemotion classifying facial expressions in vr using eye tracking cameras"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"

Connected through hyperedge HE-3 (induced by source paper "bounded residual gradient networks breg net for facial affect computing"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "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"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "context aware emotion recognition networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "cbam convolutional block attention module"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "deep residual learning for image recognition"
    - "action recognition using visual attention"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "affectnet a database for facial expression valence and arousal computing in the wild"):
  with members:
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "going deeper in facial expression recognition using deep neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "deeppose human pose estimation via deep neural networks"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-6 (induced by source paper "a compact embedding for facial expression similarity"):
  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"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "self supervised learning of a facial attribute embedding from video"
    - "deep facial expression recognition a survey"
    - "deep metric learning with hierarchical triplet loss"
    - "triplet center loss for multi view 3d object retrieval"

Connected through hyperedge HE-7 (induced by source paper "local learning with deep and handcrafted features for facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "improving bag of visual words towards effective facial expressive image classification"
    - "facial expression recognition using a hybrid cnn sift aggregator"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "dsd dense sparse dense training for deep neural networks"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-8 (induced by source paper "cnn based facial affect analysis on mobile devices"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)