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: "at a glance pixel approximate entropy as a measure of line chart complexity"
  Abstract: When inspecting information visualizations under time critical settings, such as emergency response or monitoring the heart rate in a surgery room, the user only has a small amount of time to view the visualization "at a glance". In these settings, it is important to provide a quantitative measure of the visualization to understand whether or not the visualization is too "complex" to accurately judge at a glance. This paper proposes Pixel Approximate Entropy (PAE), which adapts the approximate entropy statistical measure commonly used to quantify regularity and unpredictability in time-series data, as a measure of visual complexity for line charts. We show that PAE is correlated with user-perceived chart complexity, and that increased chart PAE correlates with reduced judgement accuracy. We also find that the correlation between PAE values and participants' judgment increases when the user has less time to examine the line charts.

Connected through hyperedge HE-1 (induced by source paper "what do people see in a twenty second glimpse of bivariate vector field visualizations"):
  with members:
    - "toward a deep understanding of what makes a scientific visualization memorable"
    - "measuring the effects of scalar and spherical colormaps on ensembles of dmri tubes"
    - "bivariate separable dimension glyphs can improve visual analysis of holistic features"
    - "what can be predicted from six seconds of driver glances"
    - "what do different evaluation metrics tell us about saliency models"

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"
    - "many to many geographically embedded flow visualisation an evaluation"
    - "task based effectiveness of basic visualizations"
    - "what is visualization really for"

Connected through hyperedge HE-3 (induced by source paper "topolines topological smoothing for line charts"):
  with members:
    - "the topology toolkit"
    - "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)