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: "ui net interactive artificial neural networks for iterative image segmentation based on a user model"
  Abstract: For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient. We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.

Connected through hyperedge HE-1 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "radiotherapy target contouring with convolutional gated graph neural network"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "brain tumor segmentation with deep neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "gated graph sequence neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "a semi automated usability evaluation framework for interactive image segmentation systems"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "swipecut interactive segmentation with diversified seed proposals"
    - "robust seed mask generation for interactive image segmentation"
    - "deep interactive object selection"

Connected through hyperedge HE-4 (induced by source paper "guide me interacting with deep networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "real time user guided image colorization with learned deep priors"
    - "film visual reasoning with a general conditioning layer"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-5 (induced by source paper "iterative interaction training for segmentation editing networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "iteratively trained interactive segmentation"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "deeply supervised cnn for prostate segmentation"
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)