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: "convolutional sparse kernel network for unsupervised medical image analysis"
  Abstract: Abstract   The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) we extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.

Connected through hyperedge HE-1 (induced by source paper "learning to segment images with classification labels"):
  with members:
    - "multimodal self supervised learning for medical image analysis"
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "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-2 (induced by source paper "unsupervised feature learning with k means and an ensemble of deep convolutional neural networks for medical image classification"):
  with members:
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"
    - "multiple kernel sparse representations for supervised and unsupervised learning"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional kernel 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)