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: "spring embedders and force directed graph drawing algorithms"
  Abstract: Force-directed algorithms are among the most flexible methods for calculating layouts of simple undirected graphs. Also known as spring embedders, such algorithms calculate the layout of a graph using only information contained within the structure of th eg raph itself, rather than relying on domain-specific knowledge. Graphs drawn with these algorithms tend to be aesthetically pleasing, exhibit symmetries, and tend to produce crossing-free layouts for planar graphs. In this survey we consider several classical algorithms, starting from Tutte’s 1963 barycentric method, and including recent scalable multiscale methods for large and dynamic graphs.

Connected through hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-2 (induced by source paper "graph preserving grid layout a simple graph drawing method for graph classification using cnns"):
  with members:
    - "a deep generative model for graph layout"
    - "semi supervised graph classification a hierarchical graph perspective"
    - "graph u nets"
    - "simplifying graph convolutional networks"
    - "learning discrete structures for graph neural networks"
    - "graph star net for generalized multi task learning"
    - "a comprehensive survey on graph neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "tge viz transition graph embedding for visualization of plan traces and domains"):
  with members:
    - "visualizations for an explainable planning agent"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "the fast downward planning system"

Connected through hyperedge HE-4 (induced by source paper "improving the visualization of alloy instances"):
  with members:
    - "the dynalloy visualizer"

Connected through hyperedge HE-5 (induced by source paper "can gan learn topological features of a graph"):
  with members:
    - "nips 2016 tutorial generative adversarial networks"
    - "energy based generative adversarial network"
    - "semi supervised classification with graph convolutional networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep convolutional networks on graph structured data"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "deep community detection"

Connected through hyperedge HE-6 (induced by source paper "automated lane detection in crowds using proximity graphs"):
  with members:
    - "crowded scene analysis a survey"
    - "fully convolutional neural networks for crowd 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.CG(Computational Geometry)