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: "microscopy cell segmentation via adversarial neural networks"
  Abstract: We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "joint multi frame detection and segmentation for multi cell tracking"):
  with members:
    - "unet a nested u net architecture for medical image segmentation"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "stacked hourglass networks for human pose estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deeper into convolutional networks for learning video representations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "accurate nuclear segmentation with center vector encoding"):
  with members:
    - "path aggregation network for instance segmentation"
    - "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"
    - "deep layer aggregation"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "qanet quality assurance network for image segmentation"):
  with members:
    - "yolov3 an incremental improvement"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "microscopy cell segmentation via convolutional lstm networks"):
  with members:
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "show attend and tell neural image caption generation with visual attention"

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)