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: "extracting 3d vascular structures from microscopy images using convolutional recurrent networks"
  Abstract: Vasculature is known to be of key biological significance, especially in the study of cancer. As such, considerable effort has been focused on the automated measurement and analysis of vasculature in medical and pre-clinical images. In tumors in particular, the vascular networks may be extremely irregular and the appearance of the individual vessels may not conform to classical descriptions of vascular appearance. Typically, vessels are extracted by either a segmentation and thinning pipeline, or by direct tracking. Neither of these methods are well suited to microscopy images of tumor vasculature. In order to address this we propose a method to directly extract a medial representation of the vessels using Convolutional Neural Networks. We then show that these two-dimensional centerlines can be meaningfully extended into 3D in anisotropic and complex microscopy images using the recently popularized Convolutional Long Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this hybrid convolutional-recurrent architecture over both 2D and 3D convolutional comparators.

Connected through hyperedge HE-1 (induced by source paper "deep sequential segmentation of organs in volumetric medical scans"):
  with members:
    - "densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation"
    - "lstm a search space odyssey"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "3 d convolutional neural networks for glioblastoma segmentation"

Connected through hyperedge HE-2 (induced by source paper "forecast clstm a new convolutional lstm network for cloudage nowcasting"):
  with members:
    - "hierarchical model for long term video prediction"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "deeprain convlstm network for precipitation prediction using multichannel radar data"
    - "bidirectional lstm crf models for sequence tagging"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional neural networks for segmenting 3d in vivo multiphoton images of vasculature in alzheimer disease mouse models"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "deep retinal image understanding"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep learning convolutional networks for multiphoton microscopy vasculature segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

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)