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: "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
  Abstract: In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different schemes to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.

Connected through hyperedge HE-1 (induced by source paper "medgan medical image translation using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "towards a deeper understanding of adversarial losses"
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "adversarial sparse view cbct artifact reduction"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "pros and cons of gan evaluation measures"
    - "stacked u nets a no frills approach to natural image segmentation"

Connected through hyperedge HE-2 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "generative adversarial network in medical imaging a review"
    - "fighting fake news image splice detection via learned self consistency"
    - "melanogans high resolution skin lesion synthesis with gans"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "learning implicit brain mri manifolds with deep learning"
    - "blood vessel geometry synthesis using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "mri to fdg pet cross modal synthesis using 3d u net for multi modal alzheimer s classification"):
  with members:
    - "medical image synthesis with context aware generative adversarial networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for 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)