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: "task based effectiveness of basic visualizations"
  Abstract: Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks and data attribute types. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five visualization types --- Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart --- across ten common data analysis tasks and three data attribute types using two real-world datasets. We found the effectiveness of these visualization types significantly varies across task and data attribute types, suggesting that visualization design would benefit from considering context dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "juxtaposing controlled empirical studies in visualization with topic developments in psychology"):
  with members:
    - "maps and globes in virtual reality"
    - "at a glance pixel approximate entropy as a measure of line chart complexity"
    - "many to many geographically embedded flow visualisation an evaluation"
    - "what is visualization really for"

Connected through hyperedge HE-3 (induced by source paper "task oriented optimal sequencing of visualization charts"):
  with members:
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "topolines topological smoothing for line charts"):
  with members:
    - "at a glance pixel approximate entropy as a measure of line chart complexity"
    - "the topology toolkit"

Connected through hyperedge HE-5 (induced by source paper "illusion of causality in visualized data"):
  with members:
    - "a bayesian cognition approach to improve data visualization"

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"
    - "vizml a machine learning approach to visualization recommendation"
    - "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"
    - "a syntactic neural model for general purpose code generation"

Connected through hyperedge HE-7 (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"
    - "beagle automated extraction and interpretation of visualizations from the web"

Connected through hyperedge HE-8 (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"
    - "beagle automated extraction and interpretation of visualizations from the web"

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)