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: "deep generative contrastive networks for facial expression recognition"
  Abstract: As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative expression of a query face compared to a reference face might alleviate this difficulty. In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose. The contrastive representation is calculated at the embedding layer of deep networks by comparing a given (query) image with the reference image. We attempt to utilize a generative reference image that is estimated based on the given image. Consequently, we deploy deep neural networks that embed a combination of a generative model, a contrastive model, and a discriminative model with an end-to-end training manner. In our proposed networks, we attempt to disentangle a facial expressive factor in two steps including learning of a generator network and a contrastive encoder network. We conducted extensive experiments on publicly available face expression databases (CK+, MMI, Oulu-CASIA, and in-the-wild databases) that have been widely adopted in the recent literatures. The proposed method outperforms the known state-of-the art methods in terms of the recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "deep multi facial patches aggregation network for facial expression recognition"):
  with members:
    - "learning to augment expressions for few shot fine grained facial expression recognition"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "deep facial expression recognition a survey"
    - "semantic segmentation using adversarial networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "a personalized affective memory neural model for improving emotion recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "the omg emotion behavior dataset"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "unsupervised deep representations for learning audience facial behaviors"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "emotiw 2018 audio video student engagement and group level affect prediction"

Connected through hyperedge HE-3 (induced by source paper "expertnet exigent features preservative network for facial expression recognition"):
  with members:
    - "spatio temporal facial expression recognition using convolutional neural networks and conditional random fields"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "deep residual learning for image recognition"
    - "dexpression deep convolutional neural network for expression recognition"
    - "deep temporal appearance geometry network for facial expression recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "facial motion prior networks for facial expression recognition"):
  with members:
    - "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"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "deep multi facial patches aggregation network for expression classification from face images"):
  with members:
    - "deep facial expression recognition a survey"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-6 (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-7 (induced by source paper "identity enhanced network for facial expression recognition"):
  with members:
    - "focal loss for dense object detection"
    - "funnel structured cascade for multi view face detection with alignment awareness"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"
    - "peak piloted deep network for facial expression recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-8 (induced by source paper "differential generative adversarial networks synthesizing non linear facial variations with limited number of training data"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "face aging with conditional generative adversarial networks"
    - "exprgan facial expression editing with controllable expression intensity"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "photo realistic single image super resolution using a generative adversarial network"

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)