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 "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"):
  Abstract: Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels. To leverage the performance, 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks. Moreover, the 3D Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the testing stage to interpret the behaviors of the deep neural network. 5075 non-contrast chest CT scans were used as training, validation and testing datasets. Baseline performance was assessed on the same cohort. From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0.9272) and AUC (0.9627).

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d aggregated faster r cnn for general lesion detection")
  Member "synseg net synthetic segmentation without target modality ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on direct distribution matching for adapting segmentation networks")
  Member "less is more simultaneous view classification and landmark detection for abdominal ultrasound images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "oralcam enabling self examination and awareness of oral health using a smartphone camera")
  Member "attention gated networks for improving ultrasound scan plane detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "inter scanner harmonization of high angular resolution dw mri using null space deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the value of nullspace tuning using partial label information")
  Member "an automatic deep learning approach for coronary artery calcium segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning in cardiology")
  Member "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation")

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)