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 "what do people see in a twenty second glimpse of bivariate vector field visualizations"):
  Abstract: Little is known about how people learn from a brief glimpse of three-dimensional (3D) bivariate vector field visualizations and about how well visual features can guide behavior. Here we report empirical study results on the use of color, texture, and length to guide viewing of bivariate glyphs: these three visual features are mapped to the first integer variable (v1) and length to the second quantitative variable (v2). Participants performed two tasks within 20 seconds: (1) MAX: find the largest v2 when v1 is fixed; (2) SEARCH: find a specific bivariate variable shown on the screen in a vector field. Our first study with eighteen participants performing these tasks showed that the randomized vector positions, although they lessened viewers' ability to group vectors, did not reduce task accuracy compared to structured vector fields. This result may support that these color, texture, and length can provide to a certain degree, guide viewers' attention to task-relevant regions. The second study measured eye movement to quantify viewers' behaviors with three-errors (scanning, recognition, and decision errors) and one-behavior (refixation) metrics. Our results showed two dominant search strategies: drilling and scanning. Coloring tended to restrict eye movement to the task-relevant regions of interest, enabling drilling. Length tended to support scanners who quickly wandered around at different v1 levels. Drillers had significantly less errors than scanners and the error rates for color and texture were also lowest. And length had limited discrimination power than color and texture as a 3D visual guidance. Our experiment results may suggest that using categorical visual feature could help obtain the global structure of a vector field visualization. We provide the first benchmark of the attention cost of seeing a bivariate vector on average about 5 items per second.

Includes members:
  Member "toward a deep understanding of what makes a scientific visualization memorable":
    also connected through other hyperedges:
      (none)
  Member "measuring the effects of scalar and spherical colormaps on ensembles of dmri tubes":
    also connected through other hyperedges:
      (none)
  Member "at a glance pixel approximate entropy as a measure of line chart complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "juxtaposing controlled empirical studies in visualization with topic developments in psychology")
  Member "bivariate separable dimension glyphs can improve visual analysis of holistic features":
    also connected through other hyperedges:
      (none)
  Member "what can be predicted from six seconds of driver glances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mit advanced vehicle technology study large scale naturalistic driving study of driver behavior and interaction with automation")
  Member "what do different evaluation metrics tell us about saliency models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")

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.GR(Graphics)