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 "credibility evaluation of income data with hierarchical correlation reconstruction"):
  Abstract: In situations like tax declarations or analyzes of household budgets we would like to automatically evaluate credibility of exogenous variable (declared income) based on some available (endogenous) variables - we want to build a model and train it on provided data sample to predict (conditional) probability distribution of exogenous variable based on values of endogenous variables. Using Polish household budget survey data there will be discussed simple and systematic adaptation of hierarchical correlation reconstruction (HCR) technique for this purpose, which allows to combine interpretability of statistics with modelling of complex densities like in machine learning. For credibility evaluation we normalize marginal distribution of predicted variable to $\rho\approx 1$ uniform distribution on $[0,1]$ using empirical distribution function $(x=EDF(y)\in[0,1])$, then model density of its conditional distribution $(\textrm{Pr}(x_0|x_1 x_2\ldots))$ as a linear combination of orthonormal polynomials using coefficients modelled as linear combinations of features of the remaining variables. These coefficients can be calculated independently, have similar interpretation as cumulants, additionally allowing to directly reconstruct probability distribution. Values corresponding to high predicted density can be considered as credible, while low density suggests disagreement with statistics of data sample, for example to mark for manual verification a chosen percentage of data points evaluated as the least credible.

Includes members:
  Member "hierarchical correlation reconstruction with missing data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting statistical dependencies of time series with hierarchical correlation reconstruction")
  Member "exploiting statistical dependencies of time series with hierarchical correlation reconstruction":
    also connected through other hyperedges:
      (none)
  Member "rapid parametric density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polynomial based rotation invariant features")

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)