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 "weakly supervised lesion segmentation on ct scans using co segmentation"):
  Abstract: Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. This task, however, is very challenging since manual segmentation is prohibitively time-consuming, expensive, and requires professional knowledge. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors (RECIST). Although these markers lack detailed information about the lesion regions, they are commonly found in hospitals' picture archiving and communication systems (PACS). Thus, these markers have the potential to serve as a powerful source of weak-supervision for 2D lesion segmentation. To approach this problem, this paper proposes a convolutional neural network (CNN) based weakly-supervised lesion segmentation method, which first generates the initial lesion masks from the RECIST measurements and then utilizes co-segmentation to leverage lesion similarities and refine the initial masks. In this work, an attention-based co-segmentation model is adopted due to its ability to learn more discriminative features from a pair of images. Experimental results on the NIH DeepLesion dataset demonstrate that the proposed co-segmentation approach significantly improves lesion segmentation performance, e.g the Dice score increases about 4.0% (from 85.8% to 89.8%).

Includes members:
  Member "weakly supervised lesion co segmentation on ct scans":
    also connected through other hyperedges:
      (none)
  Member "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised lesion co segmentation on ct scans")
  Member "abnormal chest x ray identification with generative adversarial one class classifier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised lesion co segmentation on ct scans")
  Member "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised lesion co segmentation on ct scans")
  Member "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised lesion co segmentation on ct scans")
  Member "semantic aware attention based deep object co segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zero shot video object segmentation via attentive graph neural networks")
  Member "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised lesion co segmentation on ct scans")
  Member "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised lesion co segmentation on ct scans")

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)