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: "personalized pancreatic tumor growth prediction via group learning"
  Abstract: Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method.

Connected through hyperedge HE-1 (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"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "time dependent representation for neural event sequence prediction"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-2 (induced by source paper "fusionnet incorporating shape and texture for abnormality detection in 3d abdominal ct scans"):
  with members:
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "joint shape representation and classification for detecting pdac"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-3 (induced by source paper "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"):
  with members:
    - "supervised and unsupervised tumor characterization in the deep learning era"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "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-4 (induced by source paper "convolutional invasion and expansion networks for tumor growth prediction"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "deeppap deep convolutional networks for cervical cell classification"
    - "unsupervised learning for physical interaction through video prediction"
    - "long term image boundary extrapolation"
    - "convolutional two stream network fusion for video action recognition"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-5 (induced by source paper "joint shape representation and classification for detecting pdac"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "discriminative recurrent sparse auto encoders"

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)