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 "omega net fully automatic multi view cardiac mr detection orientation and segmentation with deep neural networks"):
  Abstract: Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present ${\Omega}$-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous detection, transformation into a canonical orientation, and semantic segmentation. First, a coarse-grained segmentation is performed on the input image, second, the features learned during this coarse-grained segmentation are used to predict the parameters needed to transform the input image into a canonical orientation, and third, a fine-grained segmentation is performed on the transformed image. In this work, ${\Omega}$-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA, four-chamber, 4C, two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42) and healthy control subjects (N = 21). Network performance as measured by weighted foreground intersection-over-union (IoU) was substantially improved in the best-performing ${\Omega}$- Net compared with U-Net segmentation without detection or orientation (0.858 vs 0.834). We believe this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.

Includes members:
  Member "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning in cardiology")
  Member "fastventricle cardiac segmentation with enet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning in cardiology")
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "a fully convolutional neural network for cardiac segmentation in short axis mri":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking fully convolutional networks for the analysis of photoluminescence wafer images")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "multi scale context aggregation by dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention guided chained context aggregation for 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)