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: "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
  Abstract: With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networks have been proposed for 2D and 3D segmentation with promising results. Nevertheless, most networks only handle relatively small numbers of labels (<10), and there are very limited works on handling highly unbalanced object sizes especially in 3D segmentation. In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures. By combining skip connections and deep supervision with respect to the computational feasibility of 3D segmentation, we propose a fast converging and computationally efficient network architecture for accurate segmentation. Furthermore, inspired by the concept of focal loss, we propose an exponential logarithmic loss which balances the labels not only by their relative sizes but also by their segmentation difficulties. We achieve an average Dice coefficient of 82% on brain segmentation with 20 labels, with the ratio of the smallest to largest object sizes as 0.14%. Less than 100 epochs are required to reach such accuracy, and segmenting a \(128 \times 128 \times 128\) volume only takes around 0.4 s.

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"
    - "focal loss for dense object detection"
    - "the microsoft 2017 conversational speech recognition system"

Connected through hyperedge HE-2 (induced by source paper "class conditional domain adaptation on semantic segmentation"):
  with members:
    - "domain adaptation for semantic segmentation with maximum squares loss"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "point cloud instance segmentation using probabilistic embeddings"):
  with members:
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "dynamic scale inference by entropy minimization"
    - "associatively segmenting instances and semantics in point clouds"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "bag of tricks for image classification with convolutional neural networks"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"

Connected through hyperedge HE-4 (induced by source paper "improved inference via deep input transfer"):
  with members:
    - "combo loss handling input and output imbalance in multi organ segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "select attend and transfer light learnable skip connections"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "chest x ray analysis of tuberculosis by deep learning with segmentation and augmentation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (induced by source paper "a multi path 2 5 dimensional convolutional neural network system for segmenting stroke lesions in brain mri images"):
  with members:
    - "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"
    - "brain tumor segmentation with deep neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "psacnn pulse sequence adaptive fast whole brain segmentation"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "pulse sequence resilient fast brain segmentation"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-7 (induced by source paper "3d whole brain segmentation using spatially localized atlas network tiles"):
  with members:
    - "compnet complementary segmentation network for brain mri extraction"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "4d multi atlas label fusion using longitudinal images"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-8 (induced by source paper "machine learning on biomedical images interactive learning transfer learning class imbalance and beyond"):
  with members:
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "attention u net learning where to look for the pancreas"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "focal loss for dense object detection"
    - "towards alzheimer s disease classification through transfer learning"
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "u net convolutional networks for biomedical image segmentation"
    - "how transferable are features in deep neural networks"

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)