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: "neural turtle graphics for modeling city road layouts"
  Abstract: We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the road layout using a graph where nodes in the graph represent control points and edges in the graph represent road segments. NTG is a sequential generative model parameterized by a neural network. It iteratively generates a new node and an edge connecting to an existing node conditioned on the current graph. We train NTG on Open Street Map data and show that it outperforms existing approaches using a set of diverse performance metrics. Moreover, our method allows users to control styles of generated road layouts mimicking existing cities as well as to sketch parts of the city road layout to be synthesized. In addition to synthesis, the proposed NTG finds uses in an analytical task of aerial road parsing. Experimental results show that it achieves state-of-the-art performance on the SpaceNet dataset.

Connected through hyperedge HE-1 (induced by source paper "image conditioned graph generation for road network extraction"):
  with members:
    - "path augmented graph transformer network"
    - "roberta a robustly optimized bert pretraining approach"
    - "efficient graph generation with graph recurrent attention networks"
    - "spacenet a remote sensing dataset and challenge series"
    - "neural particle smoothing for sampling from conditional sequence models"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-2 (induced by source paper "efficient graph generation with graph recurrent attention networks"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-3 (induced by source paper "towards reliable evaluation of road network reconstructions"):
  with members:
    - "joint segmentation and path classification of curvilinear structures"
    - "spacenet a remote sensing dataset and challenge series"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "beyond the pixel wise loss for topology aware delineation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "modelling urban networks using variational autoencoders"):
  with members:
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "modeling urbanization patterns with generative adversarial networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet"
    - "why should i trust you explaining the predictions of any classifier"
    - "on the quantitative analysis of decoder based generative models"

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)