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: "aesthetic discrimination of graph layouts"
  Abstract: This paper addresses the following basic question: given two layouts of the same graph, which one is more aesthetically pleasing? We propose a neural network-based discriminator model trained on a labeled dataset that decides which of two layouts has a higher aesthetic quality. The feature vectors used as inputs to the model are based on known graph drawing quality metrics, classical statistics, information-theoretical quantities, and two-point statistics inspired by methods of condensed matter physics. The large corpus of layout pairs used for training and testing is constructed using force-directed drawing algorithms and the layouts that naturally stem from the process of graph generation. It is further extended using data augmentation techniques. The mean prediction accuracy of our model is 95.70%, outperforming discriminators based on stress and on the linear combination of popular quality metrics by a statistically significant margin.

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"
    - "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"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-2 (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"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "cross sentence n ary relation extraction with graph lstms"

Connected through hyperedge HE-3 (induced by source paper "evaluating the readability of force directed graph layouts a deep learning approach"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "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"

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.DS(Data Structures and Algorithms)