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: "deep emotion facial expression recognition using attentional convolutional network"
  Abstract: Facial expression recognition has been an active research area over the past few decades, and it is still challenging due to the high intra-class variation. #R##N#Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG and LBP, followed by a classifier trained on a database of images or videos. #R##N#Most of these works perform reasonably well on datasets of images captured in a controlled condition, but fail to perform as good on more challenging datasets with more image variation and partial faces. #R##N#In recent years, several works proposed an end-to-end framework for facial expression recognition, using deep learning models. #R##N#Despite the better performance of these works, there still seems to be a great room for improvement. #R##N#In this work, we propose a deep learning approach based on attentional convolutional network, which is able to focus on important parts of the face, and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. #R##N#We also use a visualization technique which is able to find important face regions for detecting different emotions, based on the classifier's output. #R##N#Through experimental results, we show that different emotions seems to be sensitive to different parts of the face.

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"
    - "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"
    - "vgan based image representation learning for privacy preserving facial expression recognition"

Connected through hyperedge HE-2 (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 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"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "deepiris iris recognition using a deep learning approach"):
  with members:
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "fingernet pushing the limits of fingerprint recognition using convolutional neural network"):
  with members:
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "a fine grained facial expression database for end to end multi pose facial expression recognition"):
  with members:
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "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-6 (induced by source paper "audio text sentiment analysis using deep robust complementary fusion of multi features and multi modalities"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "multimodal sentiment analysis using hierarchical fusion with context modeling"
    - "deep learning for sentiment analysis a survey"
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "multimodal speech emotion recognition using audio and text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal machine learning a survey and taxonomy"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"

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)