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: "self supervised learning for spinal mris"
  Abstract: A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. The performance of this pre-trained network is then assessed on a grading classification task. We experiment on a dataset of 1016 subjects, 423 possessing follow-up scans, with the end goal of learning the disc degeneration radiological gradings attached to the intervertebral discs. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.

Connected through hyperedge HE-1 (induced by source paper "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"):
  with members:
    - "automatic segmentation of pulmonary lobes using a progressive dense v network"
    - "unsupervised representation learning by predicting image rotations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "network dissection quantifying interpretability of deep visual representations"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "progressive growing of gans for improved quality stability and variation"
    - "colorization as a proxy task for visual understanding"
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"

Connected through hyperedge HE-2 (induced by source paper "self supervised learning for cardiac mr image segmentation by anatomical position prediction"):
  with members:
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "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"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-3 (induced by source paper "shredder learning noise distributions to protect inference privacy"):
  with members:
    - "using self supervised learning can improve model robustness and uncertainty"
    - "chiron privacy preserving machine learning as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "security risks in deep learning implementations"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "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)