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: "a deep learning perspective on the origin of facial expressions"
  Abstract: Facial expressions play a significant role in human communication and behavior. Psychologists have long studied the relationship between facial expressions and emotions. Paul Ekman et al., devised the Facial Action Coding System (FACS) to taxonomize human facial expressions and model their behavior. The ability to recognize facial expressions automatically, enables novel applications in fields like human-computer interaction, social gaming, and psychological research. There has been a tremendously active research in this field, with several recent papers utilizing convolutional neural networks (CNN) for feature extraction and inference. In this paper, we employ CNN understanding methods to study the relation between the features these computational networks are using, the FACS and Action Units (AU). We verify our findings on the Extended Cohn-Kanade (CK+), NovaEmotions and FER2013 datasets. We apply these models to various tasks and tests using transfer learning, including cross-dataset validation and cross-task performance. Finally, we exploit the nature of the FER based CNN models for the detection of micro-expressions and achieve state-of-the-art accuracy using a simple long-short-term-memory (LSTM) recurrent neural network (RNN).

Connected through hyperedge HE-1 (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-2 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-3 (induced by source paper "improving facial emotion recognition systems using gradient and laplacian images"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "facial expression recognition based on local region specific features and support vector machines"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "real time emotion recognition for gaming using deep convolutional network features"

Connected through hyperedge HE-4 (induced by source paper "emotion recognition with 4kresolution database"):
  with members:
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "deep structured learning for facial action unit intensity estimation"

Connected through hyperedge HE-5 (induced by source paper "deep facial expression recognition a survey"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep covariance descriptors for facial expression recognition"
    - "geometry contrastive gan for facial expression transfer"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "covariance pooling for facial expression recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "accurate facial parts localization and deep learning for 3d facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"

Connected through hyperedge HE-6 (induced by source paper "advanced local motion patterns for macro and micro facial expression recognition"):
  with members:
    - "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"
    - "automatic facial expression recognition using features of salient facial patches"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "facial expression recognition using facial landmark detection and feature extraction via neural networks"):
  with members:
    - "facial expression recognition based on local region specific features and support vector machines"
    - "automatic facial expression recognition using features of salient facial patches"
    - "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)