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 "nonlinear function estimation with empirical bayes and approximate message passing"):
  Abstract: Nonlinear function estimation is core to modern machine learning applications. In this paper, to perform nonlinear function estimation, we reduce a nonlinear inverse problem to a linear one using a polynomial kernel expansion. These kernels increase the feature set, and may result in poorly conditioned matrices. Nonetheless, we show several examples where the matrix in our linear inverse problem contains only mild linear correlations among columns. The coefficients vector is modeled within a Bayesian setting for which approximate message passing (AMP), an algorithmic framework for signal reconstruction, offers Bayes-optimal signal reconstruction quality. While the Bayesian setting limits the scope of our work, it is a first step toward estimation of real world nonlinear functions. The coefficients vector is estimated using two AMP-based approaches, a Bayesian one and empirical Bayes. Numerical results confirm that our AMP-based approaches learn the function better than LASSO, offering markedly lower error in predicting test data.

Includes members:
  Member "approximate message passing algorithm with universal denoising and gaussian mixture learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors")
  Member "empirical bayes and full bayes for signal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive imaging via approximate message passing with image denoising")
  Member "recovery from linear measurements with complexity matching universal signal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms")
  Member "asymptotic analysis of map estimation via the replica method and applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "sparse volterra and polynomial regression models recoverability and estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "graphical models concepts in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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.IT(Information Theory)