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 recognition of facial displays of unfelt emotions"
  Abstract: Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase.

Connected through hyperedge HE-1 (induced by source paper "chalearn looking at people isogd and congd large scale rgb d gesture recognition"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "rank pooling for action recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "convolutional pose machines"
    - "large scale continuous gesture recognition using convolutional neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "island loss for learning discriminative features in facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-4 (induced by source paper "deep structure inference network for facial action unit recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep structured learning for facial action unit intensity estimation"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "action unit detection with region adaptation multi labeling learning and optimal temporal fusing"
    - "structured feature learning for pose estimation"
    - "crf cnn modeling structured information in human pose estimation"
    - "conditional random fields as recurrent neural networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"

Connected through hyperedge HE-5 (induced by source paper "deep covariance descriptors for facial expression recognition"):
  with members:
    - "end to end multimodal emotion recognition using deep neural networks"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "relaxed spatio temporal deep feature aggregation for real fake expression prediction"):
  with members:
    - "learnable pooling with context gating for video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"
    - "long term temporal convolutions for action recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "compact bilinear pooling"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)