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: "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
  Abstract: We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underlying idea is to assign artificial labels to abundantly available unlabeled medical images and, through a process known as surrogate supervision, pre-train a deep neural network model for the target medical image analysis task lacking sufficient labeled training data. In particular, we employ 3 surrogate supervision schemes, namely rotation, reconstruction, and colorization, in 4 different medical imaging applications representing classification and segmentation for both 2D and 3D medical images. 3 key findings emerge from our research: 1) pre-training with surrogate supervision is effective for small training sets; 2) deep models trained from initial weights pre-trained through surrogate supervision outperform the same models when trained from scratch, suggesting that pre-training with surrogate supervision should be considered prior to training any deep 3D models; 3) pre-training models in the medical domain with surrogate supervision is more effective than transfer learning from an unrelated domain (e.g., natural images), indicating the practical value of abundant unlabeled medical image data.

Connected through hyperedge HE-1 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "self supervised learning for cardiac mr image segmentation by anatomical position prediction"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-2 (induced by source paper "learning to segment images with classification labels"):
  with members:
    - "convolutional sparse kernel network for unsupervised medical image analysis"
    - "multimodal self supervised learning for medical image analysis"
    - "unsupervised learning for cell level visual representation in histopathology images with generative adversarial networks"
    - "a survey of crowdsourcing in medical image analysis"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"
    - "unsupervised representation learning by predicting image rotations"
    - "building medical image classifiers with very limited data using segmentation networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning for cardiac mr image segmentation by anatomical position prediction"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
    - "multi task self supervised visual learning"
    - "self supervised learning for spinal mris"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-4 (induced by source paper "learning improved representations by transferring incomplete evidence across heterogeneous tasks"):
  with members:
    - "detecting fake news with weak social supervision"
    - "learning to self train for semi supervised few shot classification"
    - "evidence transfer for improving clustering tasks using external categorical evidence"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning without forgetting"
    - "very deep convolutional networks for large scale image recognition"
    - "efficient estimation of word representations in vector space"
    - "identifying mislabeled training data"

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)