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: "combining data driven and model driven methods for robust facial landmark detection"
  Abstract: Facial landmark detection is an important yet challenging task for real-world computer vision applications. This paper proposes an effective and robust approach for facial landmark detection by combining data- and model-driven methods. First, a fully convolutional network (FCN) is trained to compute response maps of all facial landmark points. Such a data-driven method could make full use of holistic information in a facial image for global estimation of facial landmarks. After that, the maximum points in the response maps are fitted with a pre-trained point distribution model (PDM) to generate the initial facial shape. This model-driven method is able to correct the inaccurate locations of outliers by considering the shape prior information. Finally, a weighted version of regularized landmark mean-shift (RLMS) is employed to fine-tune the facial shape iteratively. This estimation-correction-tuning process perfectly combines the advantages of the global robustness of the data-driven method (FCN), outlier correction capability of the model-driven method (PDM), and non-parametric optimization of RLMS. Results of extensive experiments demonstrate that our approach achieves state-of-the-art performances on challenging data sets, including 300W, AFLW, AFW, and COFW. The proposed method is able to produce satisfying detection results on face images with exaggerated expressions, large head poses, and partial occlusions.

Connected through hyperedge HE-1 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "unet a nested u net architecture for medical image segmentation"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "model driven deep learning for physical layer communications"
    - "joint cs mri reconstruction and segmentation with a unified deep network"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"):
  with members:
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep joint face hallucination and recognition"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning residual images for face attribute manipulation"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"

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)