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: "beagle automated extraction and interpretation of visualizations from the web"
  Abstract: How common is interactive visualization on the web? "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 86% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare in practice. Our findings also indicate that users may prefer tools that emphasize a succinct set of visualization types, and provide diverse expert visualization examples.

Connected through hyperedge HE-1 (induced by source paper "leaf qa locate encode attend for figure question answering"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "inferring and executing programs for visual reasoning"
    - "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-2 (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"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"
    - "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-3 (induced by source paper "searching the visual style and structure of d3 visualizations"):
  with members:
    - "scatteract automated extraction of data from scatter plots"
    - "converting basic d3 charts into reusable style templates"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "beyond heuristics learning visualization design"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"

Connected through hyperedge HE-5 (induced by source paper "vizml a machine learning approach to visualization recommendation"):
  with members:
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "foresight rapid data exploration through guideposts"
    - "task based effectiveness of basic visualizations"

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)