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: "the indian spontaneous expression database for emotion recognition"
  Abstract: Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing. Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications. Effective recognition of expressions depends significantly on the quality of the database used. Most well-known facial expression databases consist of posed expressions. However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms. In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin. The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants. In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion. Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions. An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference. Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.

Connected through hyperedge HE-1 (induced by source paper "iiitm face a database for facial attribute detection in constrained and simulated unconstrained environments"):
  with members:
    - "fairface face attribute dataset for balanced race gender and age"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "a unified view of multi label performance measures"
    - "umdfaces an annotated face dataset for training deep networks"
    - "deep residual learning for image recognition"
    - "no more pesky learning rate guessing games"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "classifying and visualizing emotions with emotional dan"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "i know how you feel emotion recognition with facial landmarks"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "i know how you feel emotion recognition with facial landmarks"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "going deeper in facial expression recognition using deep neural networks"
    - "deep residual learning for image 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)