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 "curriculum semi supervised segmentation"):
  Abstract: This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are used to effectively regularize the segmentation network, constraining softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints that tolerate uncertainties with inferred knowledge, e.g., regressed region size, and can be employed for a large variety of region attributes. We evaluated our proposed strategy for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our strategy leverages unlabeled data in more efficiently, and achieves very competitive results, approaching the performance of full-supervision.

Includes members:
  Member "constrained cnn losses for weakly supervised segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")
  Member "normalized cut loss for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "on regularized losses for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "few shot 3d multi modal medical image segmentation using generative adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi supervised few shot learning for medical image segmentation")
  Member "semi supervised deep learning for fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep co training for semi supervised image segmentation")
  Member "curriculum domain adaptation for semantic segmentation of urban scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "highly efficient salient object detection with 100k parameters")

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)