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: "facial affect estimation in the wild using deep residual and convolutional networks"
  Abstract: Automated affective computing in the wild is a challenging task in the field of computer vision. This paper presents three neural network-based methods proposed for the task of facial affect estimation submitted to the First Affect-in-the-Wild challenge. These methods are based on Inception-ResNet modules redesigned specifically for the task of facial affect estimation. These methods are: Shallow Inception-ResNet, Deep Inception-ResNet, and Inception-ResNet with LSTMs. These networks extract facial features in different scales and simultaneously estimate both the valence and arousal in each frame. Root Mean Square Error (RMSE) rates of 0.4 and 0.3 are achieved for the valence and arousal respectively with corresponding Concordance Correlation Coefficient (CCC) rates of 0.04 and 0.29 using Deep Inception-ResNet method.

Connected through hyperedge HE-1 (induced by source paper "bounded residual gradient networks breg net for facial affect computing"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "facial expression recognition from world wild web"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning grimaces by watching tv"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "persemon a deep network for joint analysis of apparent personality emotion and their relationship"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "sphereface deep hypersphere embedding for face recognition"
    - "emotion recognition in the wild using deep neural networks and bayesian classifiers"
    - "bi modal first impressions recognition using temporally ordered deep audio and stochastic visual features"
    - "deep impression audiovisual deep residual networks for multimodal apparent personality trait recognition"
    - "temporal segment networks towards good practices for deep action 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)