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: "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
  Abstract: Deep neural networks have demonstrated very promising performance on accurate segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI scans. The current deep learning approaches conduct pancreas segmentation by processing sequences of 2D image slices independently through deep, dense per-pixel masking for each image, without explicitly enforcing spatial consistency constraint on segmentation of successive slices. We propose a new convolutional/recurrent neural network architecture to address the contextual learning and segmentation consistency problem. A deep convolutional sub-network is first designed and pre-trained from scratch. The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner. Our recurrent sub-network is a type of Long short-term memory (LSTM) network that performs segmentation on an image by integrating its neighboring slice segmentation predictions, in the form of a dependent sequence processing. Additionally, a novel segmentation-direct loss function (named Jaccard Loss) is proposed and deep networks are trained to optimize Jaccard Index (JI) directly. Extensive experiments are conducted to validate our proposed deep models, on quantitative pancreas segmentation using both CT and MRI scans. Our method outperforms the state-of-the-art work on CT [11] and MRI pancreas segmentation [1], respectively.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "attention driven tree structured convolutional lstm for high dimensional data understanding"):
  with members:
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "residual attention network for image classification"
    - "unsupervised learning for physical interaction through video prediction"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "spatial temporal convolutional lstms for tumor growth prediction by learning 4d longitudinal patient data"):
  with members:
    - "convolutional invasion and expansion networks for tumor growth prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "time dependent representation for neural event sequence prediction"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-5 (induced by source paper "pan projective adversarial network for medical image segmentation"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"

Connected through hyperedge HE-6 (induced by source paper "computer aided detection of squamous carcinoma of the cervix in whole slide images"):
  with members:
    - "deep learning framework for multi class breast cancer histology image classification"
    - "cancer metastasis detection with neural conditional random field"
    - "recent advances in the applications of convolutional neural networks to medical image contour detection"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "deep retinal image understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identifying metastases in sentinel lymph nodes with deep convolutional neural networks"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-7 (induced by source paper "end to end adversarial shape learning for abdomen organ deep segmentation"):
  with members:
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep learning in radiology an overview of the concepts and a survey of the state of the art"):
  with members:
    - "segmentation of the proximal femur from mr images using deep convolutional neural networks"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "brain tumor segmentation with deep neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "focal loss for dense object detection"

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)