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 "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"):
  Abstract: The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems. In this paper, we present a new chest X-ray database, namely ChestX-ray8, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based reading chest X-rays (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems.

Includes members:
  Member "less is more zero shot learning from online textual documents with noise suppression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "medgan medical image translation using gans")
  Member "hemis hetero modal image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion")
  Member "learning to read chest x rays recurrent neural cascade model for automated image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis")
  Member "deep learning for multi task medical image segmentation in multiple modalities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "visual genome connecting language and vision using crowdsourced dense image annotations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "low shot visual object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "growing a brain fine tuning by increasing model capacity")
  Member "low shot visual recognition by shrinking and hallucinating features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting meta learning as supervised learning")

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)