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: "local learning with deep and handcrafted features for facial expression recognition"
  Abstract: We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recognition. In order to obtain automatic features, we experiment with multiple CNN architectures, pre-trained models and training procedures, e.g. Dense-Sparse-Dense. After fusing the two types of features, we employ a local learning framework to predict the class label for each test image. The local learning framework is based on three steps. First, a k-nearest neighbors model is applied for selecting the nearest training samples for an input test image. Second, a one-versus-all Support Vector Machines (SVM) classifier is trained on the selected training samples. Finally, the SVM classifier is used for predicting the class label only for the test image it was trained for. Although local learning has been used before in combination with handcrafted features, to the best of our knowledge, it has never been employed in combination with deep features. The experiments on the 2013 Facial Expression Recognition (FER) Challenge data set and the FER+ data set demonstrate that our approach achieves state-of-the-art results. With a top accuracy of 75.42% on the FER 2013 data set and 86.71% on the FER+ data set, we surpass all competition by nearly 2% on both data sets.

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"
    - "low shot learning from imaginary data"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "vgan based image representation learning for privacy preserving facial expression recognition"

Connected through hyperedge HE-2 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "frame attention networks for facial expression recognition in videos"
    - "covariance pooling for facial expression recognition"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-3 (induced by source paper "deep emotion facial expression recognition using attentional convolutional network"):
  with members:
    - "spatial temporal recurrent neural network for emotion recognition"
    - "ad net audio visual convolutional neural network for advertisement detection in videos"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "an experimental study of deep convolutional features for iris recognition"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "mask r cnn"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-4 (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"
    - "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"
    - "facial expression recognition from world wild web"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"

Connected through hyperedge HE-5 (induced by source paper "a convolutional neural network for user identification based on motion sensors"):
  with members:
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "attention is all you need"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "learning human identity from motion patterns"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-6 (induced by source paper "expression affect action unit recognition aff wild2 multi task learning and arcface"):
  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"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "arcface additive angular margin loss for deep face recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"

Connected through hyperedge HE-7 (induced by source paper "a fine grained facial expression database for end to end multi pose facial expression recognition"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "pose normalized image generation for person re identification"
    - "going deeper in facial expression recognition using deep neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "continuous trade off optimization between fast and accurate deep face detectors"):
  with members:
    - "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction"
    - "s 3 fd single shot scale invariant face detector"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)