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: "identity free facial expression recognition using conditional generative adversarial network"
  Abstract: In this paper, we proposed a novel Identity-free conditional Generative Adversarial Network (IF-GAN) to explicitly reduce inter-subject variations for facial expression recognition. Specifically, for any given input face image, a conditional generative model was developed to transform an average neutral face, which is calculated from various subjects showing neutral expressions, to an average expressive face with the same expression as the input image. Since the transformed images have the same synthetic "average" identity, they differ from each other by only their expressions and thus, can be used for identity-free expression classification. In this work, an end-to-end system was developed to perform expression transformation and expression recognition in the IF-GAN framework. Experimental results on three facial expression datasets have demonstrated that the proposed IF-GAN outperforms the baseline CNN model and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.

Connected through hyperedge HE-1 (induced by source paper "facial expression recognition using human to animated character expression translation"):
  with members:
    - "facial expression recognition using disentangled adversarial learning"
    - "deep facial expression recognition a survey"
    - "island loss for learning discriminative features in facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "context encoders feature learning by inpainting"
    - "convolutional experts constrained local model for facial landmark detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "probabilistic attribute tree in convolutional neural networks for facial expression recognition"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "island loss for learning discriminative features in facial expression recognition"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-3 (induced by source paper "oculum afficit ocular affect recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "deep facial expression recognition a survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "facial expression recognition using disentangled adversarial learning"):
  with members:
    - "deep facial expression recognition a survey"
    - "island loss for learning discriminative features in facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "convolutional experts constrained local model for facial landmark detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "facial expression representation learning by synthesizing expression images"):
  with members:
    - "facial expression recognition using human to animated character expression translation"
    - "island loss for learning discriminative features in facial expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)