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: "facenet2expnet regularizing a deep face recognition net for expression recognition"
  Abstract: Relatively small data sets available for expression recognition research make the training of deep networks for expression recognition very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redun- dant information from the pre-trained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully- connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization shows that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "joint deep learning of facial expression synthesis and recognition"):
  with members:
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "deep facial expression recognition a survey"
    - "geometry contrastive gan for facial expression transfer"
    - "sphereface deep hypersphere embedding for face recognition"
    - "geometry guided adversarial facial expression synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "deep learning for human affect recognition insights and new developments"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "regularization for deep learning a taxonomy"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "attention is all you need"
    - "understanding hidden memories of recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "frame attention networks for facial expression recognition in videos"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "deep facial expression recognition a survey"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "island loss for learning discriminative features in facial expression recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"):
  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"
    - "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"
    - "the omg emotion behavior dataset"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "audeering s approach to the one minute gradual emotion challenge"

Connected through hyperedge HE-5 (induced by source paper "a personalized affective memory neural model for improving emotion recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "the omg emotion behavior dataset"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "unsupervised deep representations for learning audience facial behaviors"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "emotiw 2018 audio video student engagement and group level affect prediction"

Connected through hyperedge HE-6 (induced by source paper "all in one facial expression transfer editing and recognition using a single network"):
  with members:
    - "explicit facial expression transfer via fine grained semantic representations"
    - "facial expression recognition using human to animated character expression translation"
    - "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets"
    - "facial expression recognition using disentangled adversarial learning"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "geometry guided adversarial facial expression synthesis"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-7 (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"
    - "facial expression recognition from world wild web"

Connected through hyperedge HE-8 (induced by source paper "distill 2md mtl data distillation based on multi dataset multi domain multi task frame work to solve face related tasksks multi task learning semi supervised learning"):
  with members:
    - "model compression via distillation and quantization"
    - "deep facial expression recognition a survey"
    - "data distillation towards omni supervised learning"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "cyclical learning rates for training neural networks"
    - "semi supervised learning with ladder 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)