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 "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems"):
  Abstract: This work aims to identify/bridge the gap between Artificial Intelligence (AI) and Healthcare sides in Japan towards developing medical AI fitting into a clinical environment in five years. Moreover, we attempt to confirm the clinical relevance for diagnosis of our research-proven pathology-aware Generative Adversarial Network (GAN)-based medical image augmentation: a data wrangling and information conversion technique to address data paucity. We hold a clinically valuable AI-envisioning workshop among 2 Medical Imaging experts, 2 physicians, and 3 Healthcare/Informatics generalists. A qualitative/quantitative questionnaire survey for 3 project-related physicians and 6 project non-related radiologists evaluates the GAN projects in terms of Data Augmentation (DA) and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and its preliminary solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). The survey confirms our pathology-aware GANs' clinical relevance as a clinical decision support system and non-expert physician training tool. Radiologists generally have high expectations for AI-based diagnosis as a reliable second opinion and abnormal candidate detection, instead of replacing them. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs. We find that better DA and expert physician training would require atypical image generation via further GAN-based extrapolation.

Includes members:
  Member "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "enforcing constraints for interpolation and extrapolation in generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "encoding invariances in deep generative models")
  Member "learning more with less gan based medical image augmentation":
    also connected through other hyperedges:
      (none)
  Member "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "coco gan generation by parts via conditional coordinating":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a u net based discriminator for generative adversarial networks")
  Member "lung nodule classification using deep local global networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gated dilated networks for lung nodule classification in ct scans")
  Member "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  Member "adaptive weighting multi field of view cnn for semantic segmentation in pathology":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trained model fusion for object detection using gating network")

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)