human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lung and pancreatic tumor characterization in the deep learning era novel supervised and unsupervised learning approaches"):
  Abstract: Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this papet, 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 with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning. 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 framework. In the  second approach , we explore an unsupervised learning algorithm to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion approaches in computer vision, we propose to use proportion-support vector machine for characterizing tumors. We also seek the answer to the fundamental question about the goodness of “deep features” for unsupervised tumor classification. We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.

Includes members:
  Member "s4nd single shot single scale lung nodule detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans")
  Member "risk stratification of lung nodules using 3d cnn based multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated dilated networks for lung nodule classification in ct scans")
  Member "deep supervision for pancreatic cyst segmentation in abdominal ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phase collaborative network for two phase medical image segmentation")
  Member "towards automatic pulmonary nodule management in lung cancer screening with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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")
  Member "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation")
  Member "plug play generative networks conditional iterative generation of images in latent space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "opengan open set generative adversarial networks")
  Member "characterization of lung nodule malignancy using hybrid shape and appearance features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans")
  Member "infogan interpretable representation learning by information maximizing generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")

Question: Please tell me which class the source paper 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)