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: "multisource transfer learning with convolutional neural networks for lung pattern analysis"
  Abstract: Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

Connected through hyperedge HE-1 (induced by source paper "unpaired multi modal segmentation via knowledge distillation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "soft labeling by distilling anatomical knowledge for improved ms lesion segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "group normalization"
    - "self supervised knowledge distillation using singular value decomposition"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-2 (induced by source paper "deep transfer learning for physiological signals"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "transfer learning for clinical time series analysis using recurrent neural networks"
    - "deep transfer learning for eeg based brain computer interface"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a unified approach to interpreting model predictions"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "gram graph based attention model for healthcare representation learning"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"):
  with members:
    - "holistic interstitial lung disease detection using deep convolutional neural networks multi label learning and unordered pooling"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "cats or cat scans transfer learning from natural or medical image source data sets"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "is the winner really the best a critical analysis of common research practice in biomedical image analysis competitions"
    - "building medical image classifiers with very limited data using segmentation networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "knowledge transfer for melanoma screening with deep learning"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "semi supervised deep learning for fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"):
  with members:
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "few shot learning for named entity recognition in medical text"):
  with members:
    - "question answering through transfer learning from large fine grained supervision data"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "named entity recognition with bidirectional lstm cnns"
    - "transfer learning for video recognition with scarce training data for deep convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "feature representation analysis of deep convolutional neural network using two stage feature transfer an application for diffuse lung disease classification"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "learning both weights and connections for efficient neural networks"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "do deep nets really need to be deep"

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)