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: "supervised and unsupervised tumor characterization in the deep learning era"
  Abstract: Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images. Any improvement in robust and accurate image-based tumor characterization can assist in determining non-invasive cancer stage, prognosis, and personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly Convolutional Neural Network (CNN), by utilizing completely 3D approach and transfer learning to address the requirements of volumetric and large amount of training data, respectively. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a graph regularized sparse Multi-Task Learning (MTL) framework. #R##N#In the second approach, we explore an unsupervised scheme in order to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches, we propose a new algorithm, proportion-SVM, to characterize tumor types. In this second approach, we also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. Finally, we study the effect of unsupervised representation learning using Generative Adversarial Networks (GAN) on classification performance. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively.

Connected through hyperedge HE-1 (induced by source paper "evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support three decades development course and future prospect"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "computer aided diagnosis of lung nodule using gradient tree boosting and bayesian optimization"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "a berkeley view of systems challenges for ai"

Connected through hyperedge HE-2 (induced by source paper "multi scale coarse to fine segmentation for screening pancreatic ductal adenocarcinoma"):
  with members:
    - "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"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "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"

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)