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: "bivariate separable dimension glyphs can improve visual analysis of holistic features"
  Abstract: We introduce the cause of the inefficiency of bivariate glyphs by defining the corresponding error. To recommend efficient and perceptually accurate bivariate-glyph design, we present an empirical study of five bivariate glyphs based on three psychophysics principles: integral-separable dimensions, visual hierarchy, and pre-attentive pop out, to choose one integral pair ($length_y-length_x$), three separable pairs ($length-color$, $length-texture$, $length_y-length_y$), and one redundant pair ($length_y-color/length_x$). Twenty participants performed four tasks requiring: reading numerical values, estimating ratio, comparing two points, and looking for extreme values among a subset of points belonging to the same sub-group. The most surprising result was that $length-texture$ was among the most effective methods, suggesting that local spatial frequency features can lead to global pattern detection that facilitate visual search in complex 3D structure. Our results also reveal the following: $length-color$ bivariate glyphs led to the most accurate answers and the least task execution time, while $length_y-length_x$ (integral) dimensions were among the worst and is not recommended; it achieved high performance only when pop-up color was added.

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"
    - "at a glance pixel approximate entropy as a measure of line chart complexity"
    - "what can be predicted from six seconds of driver glances"
    - "what do different evaluation metrics tell us about saliency models"

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.GR(Graphics)