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: "what may visualization processes optimize"
  Abstract: In this paper, we present an abstract model of visualization and inference processes, and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.

Connected through hyperedge HE-1 (induced by source paper "the validity generalizability and feasibility of summative evaluation methods in visual analytics"):
  with members:
    - "rulematrix visualizing and understanding classifiers with rules"
    - "skylens visual analysis of skyline on multi dimensional data"

Connected through hyperedge HE-2 (induced by source paper "a bounded measure for estimating the benefit of visualization"):
  with members:
    - "visual abstraction"
    - "on the upper bound of the kullback leibler divergence and cross entropy"

Connected through hyperedge HE-3 (induced by source paper "a user based visual analytics workflow for exploratory model analysis"):
  with members:
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "clustrophile 2 guided visual clustering analysis"
    - "prediction factory automated development and collaborative evaluation of predictive models"
    - "do convolutional neural networks learn class hierarchy"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"
    - "the mythos of model interpretability"

Connected through hyperedge HE-4 (induced by source paper "measures in visualization space"):
  with members:
    - "towards understanding enjoyment and flow in information visualization"
    - "understanding visualization a formal approach using category theory and semiotics"

Connected through hyperedge HE-5 (induced by source paper "visual abstraction"):
  with members:
    - "an information theoretic approach to the cost benefit analysis of visualization in virtual environments"

Connected through hyperedge HE-6 (induced by source paper "visual analytics for automated model discovery"):
  with members:
    - "clustrophile 2 guided visual clustering analysis"
    - "prediction factory automated development and collaborative evaluation of predictive models"
    - "auto keras an efficient neural architecture search system"
    - "do convolutional neural networks learn class hierarchy"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"
    - "the mythos of model interpretability"

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)