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 "a unified bayesian inference framework for generalized linear models"):
  Abstract: In this letter, we present a unified Bayesian inference framework for generalized linear models (GLM), which iteratively reduces the GLM problem to a sequence of standard linear model (SLM) problems. This framework provides new perspectives on some established GLM algorithms derived from SLM ones and also suggests novel extensions for some other SLM algorithms. Specific instances elucidated under such framework are the GLM versions of approximate message passing (AMP), vector AMP, and sparse Bayesian learning. It is proved that the resultant GLM version of AMP is equivalent to the well-known generalized approximate message passing. Numerical results for one-bit quantized compressed sensing demonstrate the effectiveness of this unified framework.

Includes members:
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "generalized expectation consistent signal recovery for nonlinear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "d oamp a denoising based signal recovery algorithm for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "vector approximate message passing for the generalized linear model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective")
  Member "denoising based vector approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plug and play methods for magnetic resonance imaging")
  Member "turbo compressed sensing with partial dft sensing matrix":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inference in multi layer networks with matrix valued unknowns")

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)