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: "customized facial constant positive air pressure cpap masks"
  Abstract: Sleep apnea is a syndrome that is characterized by sudden breathing halts while sleeping. One of the common treatments involves wearing a mask that delivers continuous air flow into the nostrils so as to maintain a steady air pressure. These masks are designed for an average facial model and are often difficult to adjust due to poor fit to the actual patient. The incompatibility is characterized by gaps between the mask and the face, which deteriorates the impermeability of the mask and leads to air leakage. We suggest a fully automatic approach for designing a personalized nasal mask interface using a facial depth scan. The interfaces generated by the proposed method accurately fit the geometry of the scanned face, and are easy to manufacture. The proposed method utilizes cheap commodity depth sensors and 3D printing technologies to efficiently design and manufacture customized masks for patients suffering from sleep apnea.

Connected through hyperedge HE-1 (induced by source paper "face manifold manifold learning for synthetic face generation"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "end to end 3d face reconstruction with deep neural networks"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "3d face reconstruction by learning from synthetic data"

Connected through hyperedge HE-2 (induced by source paper "unrestricted facial geometry reconstruction using image to image translation"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d face reconstruction with geometry details from a single image"
    - "unsupervised cross domain image generation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "image to image translation with conditional adversarial networks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "photorealistic facial texture inference using deep neural networks"

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.GR(Graphics)