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: "tversky loss function for image segmentation using 3d fully convolutional deep networks"
  Abstract: Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "neurreg neural registration and its application to image segmentation"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "neural multi scale self supervised registration for echocardiogram dense tracking"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airnet self supervised affine registration for 3d medical images using neural networks"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-3 (induced by source paper "liver lesion segmentation with slice wise 2d tiramisu and tversky loss function"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "neural network based automatic liver tumor segmentation with random forest based candidate filtering"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "squeeze and excitation networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"

Connected through hyperedge HE-5 (induced by source paper "j regularization improves imbalanced multiclass segmentation"):
  with members:
    - "panoptic segmentation"
    - "cnn based preprocessing to optimize watershed based cell segmentation in 3d confocal microscopy images"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "visualizing the loss landscape of neural nets"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "multiple sclerosis lesion synthesis in mri using an encoder decoder u net"):
  with members:
    - "asymmetric loss functions and deep densely connected networks for highly imbalanced medical image segmentation application to multiple sclerosis lesion detection"
    - "brain tumor segmentation with deep neural networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "the importance of skip connections in biomedical image segmentation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "optimizing the dice score and jaccard index for medical image segmentation theory and practice"):
  with members:
    - "no new net"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "soft labeling by distilling anatomical knowledge for improved ms lesion segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "u net convolutional networks for biomedical image segmentation"

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)