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: "optimizing filter size in convolutional neural networks for facial action unit recognition"
  Abstract: Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. #R##N#This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm.

Connected through hyperedge HE-1 (induced by source paper "h cnn spatial hashing based cnn for 3d shape analysis"):
  with members:
    - "irregular convolutional neural networks"
    - "deformable convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "octnet learning deep 3d representations at high resolutions"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-2 (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"
    - "eac net a region based deep enhancing and cropping approach for 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"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "face video generation from a single image and landmarks"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "ganimation anatomically aware facial animation from a single image"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cvae gan fine grained image generation through asymmetric training"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "you said that"

Connected through hyperedge HE-4 (induced by source paper "micro expression action unit detection withspatio temporal adaptive pooling"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "deep extreme multi label learning"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "contextual action recognition with r cnn"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-5 (induced by source paper "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "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"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-6 (induced by source paper "island loss for learning discriminative features in facial expression recognition"):
  with members:
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "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"
    - "differential angular imaging for material recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning using linear support vector machines"

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)