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: "prostate cancer diagnosis using deep learning with 3d multiparametric mri"
  Abstract: A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved the second highest AUC (0.84) in the PROSTATEx challenge. This study shows the great potential of deep learning for cancer imaging.

Connected through hyperedge HE-1 (induced by source paper "from handcrafted to deep learning based cancer radiomics challenges and opportunities"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "brain tumor type classification via capsule networks"
    - "dynamic routing between capsules"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"

Connected through hyperedge HE-2 (induced by source paper "prostate cancer detection using deep convolutional neural networks"):
  with members:
    - "identity mappings in deep residual networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "a deep dive into understanding tumor foci classification using multiparametric mri based on convolutional neural network"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-4 (induced by source paper "evaluating bayesian deep learning methods for semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "on calibration of modern neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep network flow for multi object tracking"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"

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)