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: "a fixed point model for pancreas segmentation in abdominal ct scans"
  Abstract: Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans. However, the segmentation accuracy of some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupies a large fraction of the input volume. In this paper, we formulate this problem into a fixed-point model which uses a predicted segmentation mask to shrink the input region. This is motivated by the fact that a smaller input region often leads to more accurate segmentation. In the training process, we use the ground-truth annotation to generate accurate input regions and optimize network weights. On the testing stage, we fix the network parameters and update the segmentation results in an iterative manner. We evaluate our approach on the NIH pancreas segmentation dataset, and outperform the state-of-the-art by more than 4%, measured by the average Dice-S{\o}rensen Coefficient (DSC). In addition, we report 62.43% DSC in the worst case, which guarantees the reliability of our approach in clinical applications.

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 "c2fnas coarse to fine neural architecture search for 3d medical image segmentation"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "scalable neural architecture search for 3d medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "thickened 2d networks for efficient 3d medical image segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "long term feature banks for detailed video understanding"
    - "compact generalized non local network"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "differentiable learning to normalize via switchable normalization"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"

Connected through hyperedge HE-4 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unet a nested u net architecture for medical image segmentation"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "model driven deep learning for physical layer communications"
    - "joint cs mri reconstruction and segmentation with a unified deep network"

Connected through hyperedge HE-5 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention u net learning where to look for the pancreas"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"

Connected through hyperedge HE-6 (induced by source paper "phase collaborative network for two phase medical image segmentation"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-7 (induced by source paper "deep q learning driven ct pancreas segmentation with geometry aware u net"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "detection aided liver lesion segmentation using deep learning"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deformable convolutional networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "tree structured reinforcement learning for sequential object localization"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"

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)