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: "one button machine for automating feature engineering in relational databases"
  Abstract: Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16% to 24% data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost.

Connected through hyperedge HE-1 (induced by source paper "benchmark and survey of automated machine learning frameworks"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning in practice state of the art and recent results"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "automl a survey of the state of the art"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"

Connected through hyperedge HE-2 (induced by source paper "supervised learning on relational databases with graph neural networks"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "tabnet attentive interpretable tabular learning"
    - "relational pooling for graph representations"
    - "neural feature learning from relational database"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "trust in automl exploring information needs for establishing trust in automated machine learning systems"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"
    - "human ai collaboration in data science exploring data scientists perceptions of automated ai"
    - "survey on automated machine learning"

Connected through hyperedge HE-4 (induced by source paper "autoaiviz opening the blackbox of automated artificial intelligence with conditional parallel coordinates"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "how do data science workers collaborate roles workflows and tools"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "model cards for model reporting"
    - "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"

Connected through hyperedge HE-5 (induced by source paper "safe scalable automatic feature engineering framework for industrial tasks"):
  with members:
    - "interpretable mtl from heterogeneous domains using boosted tree"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "automl a survey of the state of the art"
    - "survey on automated machine learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "optimized cost per click in taobao display advertising"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-6 (induced by source paper "survey on automated machine learning"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning state of the art and open challenges"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "the autofeat python library for automatic feature engineering and selection"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "feature engineering for predictive modeling using reinforcement learning"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "extrapolation and learning equations"
    - "why should i trust you explaining the predictions of any classifier"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deep learning of representations looking forward"

Connected through hyperedge HE-8 (induced by source paper "human ai collaboration in data science exploring data scientists perceptions of automated ai"):
  with members:
    - "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"
    - "meta learning a survey"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "the many faces of data centric workflow optimization a survey"
    - "data vision learning to see through algorithmic abstraction"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

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.DB(Databases)