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 "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  Abstract: Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions. However, as we show, value-based discrimination between the predictions from the segmentation network and ground-truth annotations can hinder the training process from learning to improve structural qualities as well as disabling the network from properly expressing uncertainties. In this paper, we rethink adversarial training for semantic segmentation and propose to formulate the fake/real discrimination framework with a correct/incorrect training objective. More specifically, we replace the discriminator with a "gambler" network that learns to spot and distribute its budget in areas where the predictions are clearly wrong, while the segmenter network tries to leave no clear clues for the gambler where to bet. Empirical evaluation on two road-scene semantic segmentation tasks shows that not only does the proposed method re-enable expressing uncertainties, it also improves pixel-wise and structure-based metrics.

Includes members:
  Member "understanding deep learning techniques for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "edc3 ensemble of deep classifiers using class specific copula functions to improve semantic image segmentation")
  Member "adversarial structure matching loss for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning structure via consensus for face segmentation and parsing")
  Member "star shape prior in fully convolutional networks for skin lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a mutual bootstrapping model for automated skin lesion segmentation and classification")
  Member "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ct organ segmentation using gpu data augmentation unsupervised labels and iou loss")
  Member "efficient relaxations for dense crfs with sparse higher order potentials":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gated crf loss for weakly supervised semantic image segmentation")
  Member "segan adversarial network with multi scale l 1 loss for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "adversarial learning for semi supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment")

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)