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: "do deep neural networks learn facial action units when doing expression recognition"
  Abstract: Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn. In this work, not only do we show that CNNs can achieve strong performance, but we also introduce an approach to decipher which portions of the face influence the CNN's predictions. First, we train a zero-bias CNN on facial expression data and achieve, to our knowledge, state-of-the-art performance on two expression recognition benchmarks: the extended Cohn-Kanade (CK+) dataset and the Toronto Face Dataset (TFD). We then qualitatively analyze the network by visualizing the spatial patterns that maximally excite different neurons in the convolutional layers and show how they resemble Facial Action Units (FAUs). Finally, we use the FAU labels provided in the CK+ dataset to verify that the FAUs observed in our filter visualizations indeed align with the subject's facial movements.

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 "efficient facial feature learning with wide ensemble based 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"
    - "cnn based facial affect analysis on mobile devices"
    - "meal multi model ensemble via adversarial learning"
    - "efficient diverse ensemble for discriminative co tracking"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "xception deep learning with depthwise separable convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "deep multi facial patches aggregation network for facial expression recognition"):
  with members:
    - "learning to augment expressions for few shot fine grained facial expression recognition"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "deep facial expression recognition a survey"
    - "deep generative contrastive networks for facial expression recognition"
    - "semantic segmentation using adversarial networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (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-5 (induced by source paper "low cost transfer learning of face tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "taskonomy disentangling task transfer learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "facial expression restoration based on improved graph convolutional networks"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "rain streak removal for single image via kernel guided cnn"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"

Connected through hyperedge HE-7 (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"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "understanding unconventional preprocessors in deep convolutional neural networks for face identification"):
  with members:
    - "a survey on deep transfer learning"
    - "ring loss convex feature normalization for face recognition"
    - "expressing facial structure and appearance information in frequency domain for face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "how image degradations affect deep cnn based face recognition"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "going deeper in facial expression recognition using deep neural 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)