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: "vizml a machine learning approach to visualization recommendation"
  Abstract: Data visualization should be accessible for all analysts with data, not just the few with technical expertise. Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify five key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X- or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models. We report and interpret feature importances from one of these baseline models. To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting consensus visualization type, and exceeds that of other ML-based systems.

Connected through hyperedge HE-1 (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "dvqa understanding data visualizations via question answering"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "learning visual importance for graphic designs and data visualizations"
    - "understanding infographics through textual and visual tag prediction"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "vismaker a question oriented visualization recommender system for data exploration"):
  with members:
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository"):
  with members:
    - "idebench a benchmark for interactive data exploration"
    - "beyond heuristics learning visualization design"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"
    - "beagle automated extraction and interpretation of visualizations from the web"
    - "squad 100 000 questions for machine comprehension of text"
    - "boomerang rebounding the consequences of reputation feedback on crowdsourcing platforms"
    - "viziometrics analyzing visual information in the scientific literature"

Connected through hyperedge HE-4 (induced by source paper "text to viz automatic generation of infographics from proportion related natural language statements"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "datasite proactive visual data exploration with computation of insight based recommendations"
    - "textual analogy parsing what s shared and what s compared among analogous facts"
    - "foresight recommending visual insights"
    - "understanding infographics through textual and visual tag prediction"
    - "convolutional neural network architectures for matching natural language sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "task oriented optimal sequencing of visualization charts"):
  with members:
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"

Connected through hyperedge HE-6 (induced by source paper "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"):
  with members:
    - "beyond heuristics learning visualization design"
    - "tree to tree neural networks for program translation"
    - "foresight recommending visual insights"
    - "progressive growing of gans for improved quality stability and variation"
    - "pix2code generating code from a graphical user interface screenshot"
    - "massive exploration of neural machine translation architectures"
    - "task based effectiveness of basic visualizations"
    - "a syntactic neural model for general purpose code generation"

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.HC(Human-Computer Interaction)