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: "automated machine learning state of the art and open challenges"
  Abstract: With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. The main aim of these techniques is to reduce the role of the human in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. #R##N#In this paper, we present a comprehensive survey for the state-of-the-art efforts in tackling the CASH problem. In addition, we highlight the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment. Furthermore, we provide comprehensive coverage for the various tools and frameworks that have been introduced in this domain. Finally, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for fault diagnosis"):
  with members:
    - "survey on automated machine learning"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "smash one shot model architecture search through hypernetworks"
    - "neural architecture search with reinforcement learning"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "survey on automated machine learning"):
  with members:
    - "an open source automl benchmark"
    - "sparse recovery for orthogonal polynomial transforms"
    - "automl a survey of the state of the art"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"
    - "benchmarking automatic machine learning frameworks"

Connected through hyperedge HE-3 (induced by source paper "two stage optimization for machine learning workflow"):
  with members:
    - "quantifying error contributions of computational steps algorithms and hyperparameter choices in image classification pipelines"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "automl exploration v s exploitation"):
  with members:
    - "openml networked science in machine learning"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

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.LG(Machine Learning)