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 "an approximate message passing framework for side information"):
  Abstract: Approximate message passing (AMP) methods have gained recent traction in sparse signal recovery. Additional information about the signal, or  side information  (SI), is commonly available and can aid in efficient signal recovery. This paper presents an AMP-based framework that exploits SI and can be readily implemented in various settings for which the SI results in separable distributions. To illustrate the simplicity and applicability of our approach, this framework is applied to a Bernoulli–Gaussian (BG) model and a time-varying birth-death-drift (BDD) signal model, motivated by applications in channel estimation. We develop a suite of algorithms, called AMP-SI, and derive denoisers for the BDD and BG models. Numerical evidence demonstrating the advantages of our approach are presented alongside empirical evidence of the accuracy of a proposed state evolution.

Includes members:
  Member "an analysis of state evolution for approximate message passing with side information":
    also connected through other hyperedges:
      (none)
  Member "plug in estimation in high dimensional linear inverse problems a rigorous analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "finite sample analysis of approximate message passing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "state evolution for approximate message passing with non separable functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimized deployment of millimeter wave networks for in venue regions with stochastic users orientation")
  Member "mutual information and optimality of approximate message passing in random linear estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "the replica symmetric prediction for compressed sensing with gaussian matrices is exact":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "optimal trade offs in multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an overview of multi processor approximate message passing")

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)