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: "scatteract automated extraction of data from scatter plots"
  Abstract: Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.

Connected through hyperedge HE-1 (induced by source paper "plotqa reasoning over scientific plots"):
  with members:
    - "towards vqa models that can read"
    - "bilinear attention networks"
    - "dvqa understanding data visualizations via question answering"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "mask r cnn"
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-2 (induced by source paper "answering questions about data visualizations using efficient bimodal fusion"):
  with members:
    - "towards vqa models that can read"
    - "answer them all toward universal visual question answering models"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "tallyqa answering complex counting questions"
    - "figurenet a deep learning model for question answering on scientific plots"
    - "dvqa understanding data visualizations via question answering"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-3 (induced by source paper "data interpretation over plots"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "a simple neural network module for relational reasoning"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "mask r cnn"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "ssd single shot multibox detector"
    - "a diagram is worth a dozen images"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-4 (induced by source paper "leaf qa locate encode attend for figure question answering"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "inferring and executing programs for visual reasoning"
    - "beagle automated extraction and interpretation of visualizations from the web"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "visual7w grounded question answering in images"

Connected through hyperedge HE-5 (induced by source paper "data extraction from charts via single deep neural network"):
  with members:
    - "a simple neural network module for relational reasoning"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "detecting oriented text in natural images by linking segments"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "detecting text in natural image with connectionist text proposal network"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "searching the visual style and structure of d3 visualizations"):
  with members:
    - "beagle automated extraction and interpretation of visualizations from the web"
    - "converting basic d3 charts into reusable style templates"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "chart text a fully automated chart image descriptor"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "figureqa an annotated figure dataset for visual reasoning"):
  with members:
    - "imagination improves multimodal translation"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "learning visual reasoning without strong priors"
    - "a data driven approach for compound figure separation using convolutional neural networks"
    - "the something something video database for learning and evaluating visual common sense"
    - "image captioning with semantic attention"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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.CV(Computer Vision and Pattern Recognition)