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 "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"):
  Abstract: Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D segmentation is prohibitively time consuming, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST markers are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Toward this end, we introduce a convolutional neural network (CNN) based weakly supervised slice-propagated segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) extrapolate to segment the whole lesion slice by slice to finally obtain a volumetric segmentation. To validate the proposed method, we first test its performance on a fully annotated lymph node dataset, where WSSS performs comparably to its fully supervised counterparts. We then test on a comprehensive lesion dataset with 32,735 RECIST marks, where we report a mean Dice score of 92% on RECIST-marked slices and 76% on the entire 3D volumes.

Includes members:
  Member "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised segmentation from extreme points")
  Member "semi automatic recist labeling on ct scans with cascaded convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation")
  Member "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation")
  Member "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "progressive and multi path holistically nested neural networks for pathological lung segmentation from ct images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an end to end deep learning histochemical scoring system for breast cancer tma")
  Member "scribblesup scribble supervised convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "simple does it weakly supervised instance and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")

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)