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 "technical report a stratification approach to partial dependence for codependent variables"):
  Abstract: Model interpretability is important to machine learning practitioners, and a key component of interpretation is the characterization of partial dependence of the response variable on any subset of features used in the model. The two most common strategies for assessing partial dependence suffer from a number of critical weaknesses. In the first strategy, linear regression model coefficients describe how a unit change in an explanatory variable changes the response, while holding other variables constant. But, linear regression is inapplicable for high dimensional (p>n) data sets and is often insufficient to capture the relationship between explanatory variables and the response. In the second strategy, Partial Dependence (PD) plots and Individual Conditional Expectation (ICE) plots give biased results for the common situation of codependent variables and they rely on fitted models provided by the user. When the supplied model is a poor choice due to systematic bias or overfitting, PD/ICE plots provide little (if any) useful information. #R##N#To address these issues, we introduce a new strategy, called StratPD, that does not depend on a user's fitted model, provides accurate results in the presence codependent variables, and is applicable to high dimensional settings. The strategy works by stratifying a data set into groups of observations that are similar, except in the variable of interest, through the use of a decision tree. Any fluctuations of the response variable within a group is likely due to the variable of interest. We apply StratPD to a collection of simulations and case studies to show that StratPD is a fast, reliable, and robust method for assessing partial dependence with clear advantages over state-of-the-art methods.

Includes members:
  Member "an unexpected unity among methods for interpreting model predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations")
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)