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 "visual analytics for automated model discovery"):
  Abstract: A recent advancement in the machine learning community is the development of automated machine learning (autoML) systems, such as autoWeka or Google's Cloud AutoML, which automate the model selection and tuning process. However, while autoML tools give users access to arbitrarily complex models, they typically return those models with little context or explanation. Visual analytics can be helpful in giving a user of autoML insight into their data, and a more complete understanding of the models discovered by autoML, including differences between multiple models. In this work, we describe how visual analytics for automated model discovery differs from traditional visual analytics for machine learning. First, we propose an architecture based on an extension of existing visual analytics frameworks. Then we describe a prototype system Snowcat, developed according to the presented framework and architecture, that aids users in generating models for a diverse set of data and modeling tasks.

Includes members:
  Member "clustrophile 2 guided visual clustering analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "t visne interactive assessment and interpretation of t sne projections")
  Member "prediction factory automated development and collaborative evaluation of predictive models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a user based visual analytics workflow for exploratory model analysis")
  Member "auto keras an efficient neural architecture search system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas")
  Member "do convolutional neural networks learn class hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "rnnbow visualizing learning via backpropagation gradients in rnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual interaction with deep learning models through collaborative semantic inference")
  Member "towards better analysis of machine learning models a visual analytics perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "towards better analysis of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "what may visualization processes optimize":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the validity generalizability and feasibility of summative evaluation methods in visual analytics")

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.HC(Human-Computer Interaction)