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 "hyperdense net a hyper densely connected cnn for multi modal image segmentation"):
  Abstract: Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet, which connects each layer to every other layer in a feed-forward fashion, has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path, but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on 6-month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available at this https URL

Includes members:
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "scalable multimodal convolutional networks for brain tumour segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional networks with dense connectivity")
  Member "multi scale dense networks for resource efficient image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "isointense infant brain segmentation with a hyper dense connected convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities")
  Member "deep cnn ensembles and suggestive annotations for infant brain mri segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks")

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)