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: "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
  Abstract: Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a data set of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semisupervised fashion, utilizing both labeled and nonlabeled image regions. The experimental results show significant performance improvement with respect to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "convolutional crfs for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-2 (induced by source paper "from handcrafted to deep learning based cancer radiomics challenges and opportunities"):
  with members:
    - "brain tumor type classification via capsule networks"
    - "dynamic routing between capsules"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "prostate cancer diagnosis using deep learning with 3d multiparametric mri"
    - "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-3 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"):
  with members:
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "unbiased shape compactness for segmentation"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "gridnet with automatic shape prior registration for automatic mri cardiac segmentation"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"

Connected through hyperedge HE-4 (induced by source paper "linear and deformable image registration with 3d convolutional neural networks"):
  with members:
    - "adversarial image registration with application for mr and trus image fusion"
    - "deforming autoencoders unsupervised disentangling of shape and appearance"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "densepose dense human pose estimation in the wild"
    - "squeeze and excitation networks"
    - "deformable registration through learning of context specific metric aggregation"
    - "a deep metric for multimodal registration"
    - "spatial transformer 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)