human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "the importance of skip connections in biomedical image segmentation"
  Abstract: In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.

Connected through hyperedge HE-1 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "image to image translation for domain adaptation"
    - "residual parameter transfer for deep domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "medical image analysis using convolutional neural networks a review"
    - "skin lesion segmentation u nets versus clustering"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

Connected through hyperedge HE-3 (induced by source paper "ratlesnetv2 a fully convolutional network for rodent brain lesion segmentation"):
  with members:
    - "ablation studies in artificial neural networks"
    - "the liver tumor segmentation benchmark lits"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "visualizing the loss landscape of neural nets"
    - "rethinking atrous convolution for semantic image segmentation"
    - "densely connected convolutional networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "resunet a a deep learning framework for semantic segmentation of remotely sensed data"):
  with members:
    - "rethinking imagenet pre training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "road extraction by deep residual u net"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "context encoding for semantic segmentation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "boosting segmentation with weak supervision from image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "multi label learning with missing labels using mixed dependency graphs"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"

Connected through hyperedge HE-7 (induced by source paper "towards semi supervised segmentation via image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "pathology segmentation using distributional differences to images of healthy origin"

Connected through hyperedge HE-8 (induced by source paper "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  with members:
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "combining data driven and model driven methods for robust facial landmark detection"
    - "unet a nested u net architecture for medical image segmentation"
    - "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "model driven deep learning for physical layer communications"
    - "joint cs mri reconstruction and segmentation with a unified deep network"

Question: Please tell me which class the center node 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)