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 "combining mixture models with linear mixing updates multilayer image segmentation and synthesis"):
  Abstract: Finite mixture models for clustering can often be improved by adding a regularization that is specific to the topology of the data. For instance, mixtures are common in unsupervised image segmentation, and typically rely on averaging the posterior mixing probabilities of spatially adjacent data points (i.e. smoothing). However, this approach has had limited success with natural images. Here we make three contributions. First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases. Second, we demonstrate how to use this flexible design of the update rule to propagate segmentation information across layers of a deep network, and to train mixtures jointly across layers. Third, we compare the standard Gaussian mixture and the Student-t mixture, which is known to better capture the statistics of low-level visual features. We show that our models achieve competitive performance in natural image segmentation, with the Student-t mixtures reaching state-of-the art on boundaries scores. We also demonstrate how to exploit the resulting multilayer probabilistic generative model to synthesize naturalistic images beyond uniform textures.

Includes members:
  Member "convolutional oriented boundaries from image segmentation to high level tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "texture synthesis using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)