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: "iterative deep learning for road topology extraction"
  Abstract: This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity among the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the road network, inspired by a human delineating a complex network with the tip of their finger. We perform an extensive and comprehensive qualitative and quantitative evaluation on the road network estimation task, and show that our method also generalizes well when moving to networks of retinal vessels.

Connected through hyperedge HE-1 (induced by source paper "deep vessel segmentation by learning graphical connectivity"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "person re identification with deep similarity guided graph neural network"
    - "videos as space time region graphs"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "extraction of airways using graph neural networks"
    - "situation recognition with graph neural networks"
    - "deformable shape completion with graph convolutional autoencoders"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-2 (induced by source paper "three dimensional reconstruction of botanical trees with simulatable geometry"):
  with members:
    - "seeing tree structure from vibration"
    - "data augmented contact model for rigid body simulation"
    - "combining learned and analytical models for predicting action effects"
    - "beyond the pixel wise loss for topology aware delineation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "u net convolutional networks for biomedical image segmentation"

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)