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: "dltk state of the art reference implementations for deep learning on medical images"
  Abstract: We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data "Multi-Atlas Labeling Beyond the Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds the previously best performing CNN ($75.7$) and the accuracy of the challenge winning method ($79.0$).

Connected through hyperedge HE-1 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"

Connected through hyperedge HE-2 (induced by source paper "interactive segmentation of medical images through fully convolutional neural networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "interactive full image segmentation by considering all regions jointly"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "deep extreme cut from extreme points to object segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "representation disentanglement for multi task learning with application to fetal ultrasound"):
  with members:
    - "a two step disentanglement method"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "improving cnn training using disentanglement for liver lesion classification in ct"
    - "image to image translation for cross domain disentanglement"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "brain tissue segmentation using neuronet with different pre processing techniques"):
  with members:
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"
    - "deep residual learning for image recognition"
    - "striving for simplicity the all convolutional net"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "a gentle introduction to deep learning in medical image processing"):
  with members:
    - "towards arbitrary noise augmentation deep learning for sampling from arbitrary probability distributions"
    - "metric driven learning of correspondence weighting for 2 d 3 d image registration"
    - "adversarial patch"
    - "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
    - "image reconstruction via variational network for real time hand held sound speed imaging"
    - "deriving neural network architectures using precision learning parallel to fan beam conversion"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

Connected through hyperedge HE-6 (induced by source paper "hyperdense net a hyper densely connected cnn for multi modal image segmentation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "multi scale dense networks for resource efficient image classification"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "isointense infant brain segmentation with a hyper dense connected convolutional neural network"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"

Connected through hyperedge HE-7 (induced by source paper "small organ segmentation in whole body mri using a two stage fcn and weighting schemes"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "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"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"):
  with members:
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "on loss functions for deep neural networks in classification"
    - "identity mappings in deep residual networks"
    - "semantic segmentation using adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast r cnn"
    - "adam a method for stochastic optimization"

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)