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 "vamsa tracking provenance in data science scripts"):
  Abstract: Machine learning (ML) which was initially adopted for search ranking and recommendation systems has firmly moved into the realm of core enterprise operations like sales optimization and preventative healthcare. For such ML applications, often deployed in regulated environments, the standards for user privacy, security, and data governance are substantially higher. This imposes the need for tracking provenance end-to-end, from the data sources used for training ML models to the predictions of the deployed models. #R##N#In this work, we take a first step towards this direction by introducing the ML provenance tracking problem in the context of data science scripts. The fundamental idea is to automatically identify the relationships between data and ML models and in particular, to track which columns in a dataset have been used to derive the features of a ML model. We discuss the challenges in capturing such provenance information in the context of Python, the most common language used by data scientists. We then, present Vamsa, a modular system that extracts provenance from Python scripts without requiring any changes to the user's code. Using up to 450K real-world data science scripts from Kaggle and publicly available Python notebooks, we verify the effectiveness of Vamsa in terms of coverage, and performance. We also evaluate Vamsa's accuracy on a smaller subset of manually labeled data. Our analysis shows that Vamsa's precision and recall range from 87.5% to 98.3% and its latency is typically in the order of milliseconds for scripts of average size.

Includes members:
  Member "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy preserving weighted federated learning within oracle aided mpc framework")
  Member "provenance for interactive visualizations":
    also connected through other hyperedges:
      (none)
  Member "smoke fine grained lineage at interactive speed":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining wrong queries using small examples")
  Member "catboost unbiased boosting with categorical features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "smart data based ensemble for imbalanced big data classification")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi layer optimizations for end to end data 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.LG(Machine Learning)