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: "real time convolutional neural networks for emotion and gender classification"
  Abstract: In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.

Connected through hyperedge HE-1 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "social robots for people with developmental disabilities a user study on design features of a graphical user interface"
    - "i know how you feel emotion recognition with facial landmarks"
    - "convolutional neural networks for facial expression recognition"
    - "facial emotion detection using convolutional neural networks and representational autoencoder units"
    - "going deeper in facial expression recognition using deep neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "timeconvnets a deep time windowed convolution neural network design for real time video facial expression recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "a real time facial expression classification system using local binary patterns"

Connected through hyperedge HE-3 (induced by source paper "photo realistic facial details synthesis from single image"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "unsupervised training for 3d morphable model regression"
    - "3d face reconstruction with geometry details from a single image"
    - "expnet landmark free deep 3d facial expressions"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "extreme 3d face reconstruction seeing through occlusions"

Connected through hyperedge HE-4 (induced by source paper "improving facial emotion recognition systems using gradient and laplacian images"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "facial expression recognition based on local region specific features and support vector machines"
    - "a deep learning perspective on the origin of facial expressions"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "real time emotion recognition for gaming using deep convolutional network features"

Connected through hyperedge HE-5 (induced by source paper "a novel database of children s spontaneous facial expressions liris cse"):
  with members:
    - "what do we understand about convolutional networks"
    - "deep meta learning learning to learn in the concept space"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "graph representation for face analysis in image collections"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "face clustering representation and pairwise constraints"
    - "fine grained head pose estimation without keypoints"
    - "face detection using deep learning an improved faster rcnn approach"
    - "clustering millions of faces by identity"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "student engagement detection using emotion analysis eye tracking and head movement with machine learning"):
  with members:
    - "an introduction to convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "self normalizing neural networks"
    - "partial transfer learning with selective adversarial networks"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "are facial attributes adversarially robust"
    - "affact alignment free facial attribute classification technique"
    - "facenet a unified embedding for face recognition and clustering"

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)