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: "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
  Abstract: Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.

Connected through hyperedge HE-1 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "unsupervised medical image translation using cycle medgan"):
  with members:
    - "medgan medical image translation using gans"
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "adversarial inpainting of medical image modalities"
    - "generative adversarial network in medical imaging a review"
    - "retrospective correction of rigid and non rigid mr motion artifacts using gans"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "stacked u nets a no frills approach to natural image segmentation"
    - "generative adversarial networks for image to image translation on multi contrast mr images a comparison of cyclegan and unit"

Connected through hyperedge HE-3 (induced by source paper "el gan embedding loss driven generative adversarial networks for lane detection"):
  with members:
    - "adversarial structure matching loss for image segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "towards end to end lane detection an instance segmentation approach"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "spatial as deep spatial cnn for traffic scene understanding"

Connected through hyperedge HE-4 (induced by source paper "splenomegaly segmentation on multi modal mri using deep convolutional networks"):
  with members:
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "synseg net synthetic segmentation without target modality ground truth"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "towards adversarial retinal image synthesis"
    - "gans for biological image synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "adversarial networks for the detection of aggressive prostate cancer"

Connected through hyperedge HE-6 (induced by source paper "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "synseg net synthetic segmentation without target modality ground truth"
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "inter scanner harmonization of high angular resolution dw mri using null space deep learning"
    - "an automatic deep learning approach for coronary artery calcium segmentation"
    - "adversarial synthesis learning enables segmentation without target modality ground truth"

Connected through hyperedge HE-7 (induced by source paper "adversarial synthesis learning enables segmentation without target modality ground truth"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial 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.CV(Computer Vision and Pattern Recognition)