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: "what would a graph look like in this layout a machine learning approach to large graph visualization"
  Abstract: Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a “good” layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.

Connected through hyperedge HE-1 (induced by source paper "a deep generative model for graph layout"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "on the convergence of adam and beyond"
    - "aesthetic discrimination of graph layouts"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-2 (induced by source paper "batchlayout a batch parallel force directed graph layout algorithm in shared memory"):
  with members:
    - "multi level tree based approach for interactive graph visualization with semantic zoom"
    - "graph drawing by stochastic gradient descent"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a distributed multilevel force directed algorithm"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "modularity clustering is force directed layout"

Connected through hyperedge HE-3 (induced by source paper "deepdrawing a deep learning approach to graph drawing"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "deep learning multidimensional projections"
    - "exploring the limits of complexity a survey of empirical studies on graph visualisation"
    - "graph neural networks a review of methods and applications"
    - "visual interpretability for deep learning a survey"
    - "aesthetic discrimination of graph layouts"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-4 (induced by source paper "evaluating the readability of force directed graph layouts a deep learning approach"):
  with members:
    - "aesthetic discrimination of graph layouts"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "visual analytics for explainable deep learning"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "flud a hybrid crowd algorithm approach for visualizing biological networks"):
  with members:
    - "incentivizing high quality crowdwork"
    - "drawing arrangement graphs in small grids or how to play planarity"

Connected through hyperedge HE-6 (induced by source paper "exploring the limits of complexity a survey of empirical studies on graph visualisation"):
  with members:
    - "experimental analysis of the accessibility of drawings with few segments"
    - "many to many geographically embedded flow visualisation an evaluation"
    - "visual similarity perception of directed acyclic graphs a study on influencing factors"
    - "evaluation of two interaction techniques for visualization of dynamic graphs"
    - "on the readability of boundary labeling"
    - "node node link and node link group diagrams an evaluation"
    - "a coloring algorithm for disambiguating graph and map drawings"

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.SI(Social and Information Networks)