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: "automatic facial expression recognition using features of salient facial patches"
  Abstract: Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. These active patches are further processed to obtain the salient patches which contain discriminative features for classification of each pair of expressions, thereby selecting different facial patches as salient for different pair of expression classes. One-against-one classification method is adopted using these features. In addition, an automated learning-free facial landmark detection technique has been proposed, which achieves similar performances as that of other state-of-art landmark detection methods, yet requires significantly less execution time. The proposed method is found to perform well consistently in different resolutions, hence, providing a solution for expression recognition in low resolution images. Experiments on CK+ and JAFFE facial expression databases show the effectiveness of the proposed system.

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 "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-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"
    - "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-4 (induced by source paper "emobed strengthening monomodal emotion recognition via training with crossmodal emotion embeddings"):
  with members:
    - "dynamic difficulty awareness training for continuous emotion prediction"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "the omg emotion behavior dataset"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"

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:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a new terrain in hci emotion recognition interface using biometric data for an immersive vr experience"):
  with members:
    - "going deeper in facial expression recognition using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"):
  with members:
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "a survey on multi task learning"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "identity mappings in deep residual networks"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "advanced local motion patterns for macro and micro facial expression recognition"):
  with members:
    - "a deep learning perspective on the origin of facial expressions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
    - "reading hidden emotions spontaneous micro expression spotting and recognition"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines"

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)