human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline"):
  Abstract: Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for  automated infographics design . As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information,  i.e. , the  representation, scale, layout , and  orientation  of the timeline, and 2) the local information,  i.e. , the  location, category , and  pixels  of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques,  i.e. ,  Non-Maximum Merging, Redundancy Recover , and  DL GrabCut , to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.

Includes members:
  Member "synthetically trained icon proposals for parsing and summarizing infographics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "text to viz automatic generation of infographics from proportion related natural language statements")
  Member "vizml a machine learning approach to visualization recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vismaker a question oriented visualization recommender system for data exploration")
  Member "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vismaker a question oriented visualization recommender system for data exploration")
  Member "dvqa understanding data visualizations via question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "manymodalqa modality disambiguation and qa over diverse inputs")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "learning visual importance for graphic designs and data visualizations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "turkeyes a web based toolbox for crowdsourcing attention data")
  Member "understanding infographics through textual and visual tag prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational prototyping encoder one shot learning with prototypical images")

Question: Please tell me which class the source paper 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)