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: "eac net a region based deep enhancing and cropping approach for facial action unit detection"
  Abstract: In this paper, we propose a deep learning based approach for facial action unit detection by enhancing and cropping the regions of interest. The approach is implemented by adding two novel nets (layers): the enhancing layers and the cropping layers, to a pretrained CNN model. For the enhancing layers, we designed an attention map based on facial landmark features and applied it to a pretrained neural network to conduct enhanced learning (The E-Net). For the cropping layers, we crop facial regions around the detected landmarks and design convolutional layers to learn deeper features for each facial region (C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and Cropping (EAC) Net, which can learn both feature enhancing and region cropping functions. Our approach shows significant improvement in performance compared to the state-of-the-art methods applied to BP4D and DISFA AU datasets.

Connected through hyperedge HE-1 (induced by source paper "region based adversarial synthesis of facial action units"):
  with members:
    - "conditional adversarial synthesis of 3d facial action units"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "deep face recognition a survey"
    - "attention gan for object transfiguration in wild images"
    - "ganimation anatomically aware facial animation from a single image"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-2 (induced by source paper "facial expression restoration based on improved graph convolutional networks"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "rain streak removal for single image via kernel guided cnn"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"

Connected through hyperedge HE-3 (induced by source paper "au r cnn encoding expert prior knowledge into r cnn for action unit detection"):
  with members:
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "multi view dynamic facial action unit detection"
    - "action unit detection with region adaptation multi labeling learning and optimal temporal fusing"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "relation modeling with graph convolutional networks for facial action unit detection"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "conditional adversarial synthesis of 3d facial action units"
    - "bayesian semi supervised learning with graph gaussian processes"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "semantic relationships guided representation learning for facial action unit recognition"):
  with members:
    - "deep structure inference network for facial action unit recognition"
    - "contrast oriented deep neural networks for salient object detection"
    - "instance level salient object segmentation"
    - "gated graph sequence neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "deep residual learning for image recognition"
    - "a century of portraits a visual historical record of american high school yearbooks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "fast and effective adaptation of facial action unit detection deep model"):
  with members:
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "active one shot learning"
    - "multi label zero shot learning with structured knowledge graphs"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "targeted kernel networks faster convolutions with attentive regularization"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "interpretable convolutional neural networks"
    - "attentional pooling for action recognition"
    - "more is less a more complicated network with less inference complexity"
    - "attentive recurrent comparators"
    - "a survey of model compression and acceleration for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "facial action unit detection using attention and relation learning"):
  with members:
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

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)