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 "state evolution for approximate message passing with non separable functions"):
  Abstract: Given a high-dimensional data matrix ${\boldsymbol A}\in{\mathbb R}^{m\times n}$, Approximate Message Passing (AMP) algorithms construct sequences of vectors ${\boldsymbol u}^t\in{\mathbb R}^n$, ${\boldsymbol v}^t\in{\mathbb R}^m$, indexed by $t\in\{0,1,2\dots\}$ by iteratively applying ${\boldsymbol A}$ or ${\boldsymbol A}^{{\sf T}}$, and suitable non-linear functions, which depend on the specific application. Special instances of this approach have been developed --among other applications-- for compressed sensing reconstruction, robust regression, Bayesian estimation, low-rank matrix recovery, phase retrieval, and community detection in graphs. For certain classes of random matrices ${\boldsymbol A}$, AMP admits an asymptotically exact description in the high-dimensional limit $m,n\to\infty$, which goes under the name of `state evolution.' #R##N#Earlier work established state evolution for separable non-linearities (under certain regularity conditions). Nevertheless, empirical work demonstrated several important applications that require non-separable functions. In this paper we generalize state evolution to Lipschitz continuous non-separable nonlinearities, for Gaussian matrices ${\boldsymbol A}$. Our proof makes use of Bolthausen's conditioning technique along with several approximation arguments. In particular, we introduce a modified algorithm (called LAMP for Long AMP) which is of independent interest.

Includes members:
  Member "near optimal matrix recovery from random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "analysis of approximate message passing with a class of non separable denoisers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors")
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "dynamical functional theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding phase transitions via mutual information and mmse")
  Member "approximate message passing algorithm with universal denoising and gaussian mixture learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors")
  Member "phase transitions and sample complexity in bayes optimal matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "approximate message passing with restricted boltzmann machine priors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tree search network for sparse regression")
  Member "compressive phase retrieval via generalized approximate message passing":
    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)