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: "shaping the future through innovations from medical imaging to precision medicine"
  Abstract: Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up. In addition, due to its patient-specific nature, imaging information represents a critical component required for advancing precision medicine into clinical practice. This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power. Throughout the manuscript we will analyze the capabilities of such technologies and extrapolate on their potential impact to advance the quality of medical care, while reducing its cost.

Connected through hyperedge HE-1 (induced by source paper "deep learning based development of personalized human head model with non uniform conductivity for brain stimulation"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "lumipath towards real time physically based rendering on embedded devices"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "closing the calibration loop an inside out tracking paradigm for augmented reality in orthopedic surgery"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep learning with cinematic rendering fine tuning deep neural networks using photorealistic medical images"):
  with members:
    - "generative adversarial networks an overview"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "exploring generalization in deep learning"
    - "building disease detection algorithms with very small numbers of positive samples"
    - "fine tuning deep cnn models on specific ms coco categories"
    - "depthsynth real time realistic synthetic data generation from cad models for 2 5d recognition"

Connected through hyperedge HE-4 (induced by source paper "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "learning based regularization for cardiac strain analysis with ability for domain adaptation"

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)