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 "bilinear recovery using adaptive vector amp"):
  Abstract: We consider the problem of jointly recovering the vector band the matrix C from noisy measurements Y = A(b)C + W,where A(·) is a known affine linear function of b(i.e., A(b) = A#N# 0#N# + Σ#N# i=1#N# Q#N# b#N# i#N#A#N# i#N# with known matrices A#N# i#N#). This problem has applications in matrix completion, robust PCA, dictionary learning, self-calibration, blind deconvolution, joint-channel/symbol estimation, compressive sensing with matrix uncertainty, and many other tasks. To solve this bilinear recovery problem, we propose the Bilinear Adaptive Vector Approximate Message Passing (VAMP) algorithm. We demonstrate numerically that the proposed approach is competitive with other state-of-the-art approaches to bilinear recovery, including lifted VAMP and Bilinear Generalized Approximate Message Passing.

Includes members:
  Member "regularization by denoising clarifications and new interpretations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "plug in estimation in high dimensional linear inverse problems a rigorous analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "near optimal matrix recovery from random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "state evolution for approximate message passing with non separable functions")
  Member "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "additivity of information in multilayer networks via additive gaussian noise transforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "hybrid approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plug and play methods for magnetic resonance imaging")
  Member "state evolution for approximate message passing with non separable functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "the replica symmetric prediction for compressed sensing with gaussian matrices is exact":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")

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)