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 temporal appearance geometry network for facial expression recognition"
  Abstract: Temporal information can provide useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce this effort, a deep learning technique which is regarded as a tool to automatically extract useful features from raw data, is adopted. Our deep network is based on two different models. The first deep network extracts temporal geometry features from temporal facial landmark points, while the other deep network extracts temporal appearance features from image sequences . These two models are combined in order to boost the performance of the facial expression recognition. Through several experiments, we showed that the two models cooperate with each other. As a result, we achieved superior performance to other state-of-the-art methods in CK+ and Oulu-CASIA databases. Furthermore, one of the main contributions of this paper is that our deep network catches the facial action points automatically.

Connected through hyperedge HE-1 (induced by source paper "learn to synthesize and synthesize to learn"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "geometry contrastive gan for facial expression transfer"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-2 (induced by source paper "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"):
  with members:
    - "learn to synthesize and synthesize to learn"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

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"
    - "deep generative contrastive networks for facial expression recognition"
    - "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"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "learned 3d shape representations using fused geometrically augmented images application to facial expression and action unit detection"):
  with members:
    - "geometric convolutional neural network for analyzing surface based neuroimaging data"
    - "you only look once unified real time object detection"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "multimodal deep learning for robust rgb d object recognition"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-5 (induced by source paper "meta transfer learning for facial emotion recognition"):
  with members:
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "progressive neural networks"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "facenet2expnet regularizing a deep face recognition net for expression recognition"):
  with members:
    - "automatic attribute discovery with neural activations"
    - "going deeper in facial expression recognition using deep neural networks"
    - "lomo latent ordinal model for facial analysis in videos"
    - "peak piloted deep network for facial expression recognition"
    - "interactive inter layer activeness propagation"
    - "a large scale car dataset for fine grained categorization and verification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "the megaface benchmark 1 million faces for recognition at scale"

Connected through hyperedge HE-7 (induced by source paper "do deep neural networks learn facial action units when doing expression recognition"):
  with members:
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "striving for simplicity the all convolutional net"
    - "an analysis of unsupervised pre training in light of recent advances"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)