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 "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"):
  Abstract: In this paper, we study the compressed sensing reconstruction problem with generalized elastic net prior (GENP), where a sparse signal is sampled via a noisy underdetermined linear observation system, and an additional initial estimation of the signal (the GENP) is available during the reconstruction. We first incorporate the GENP into the LASSO and the approximate message passing (AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We then investigate the parameter selection, state evolution, and noise-sensitivity analysis of GENP-AMP. We show that, thanks to the GENP, there is no phase transition boundary in the proposed frameworks, i.e., the reconstruction error is bounded in the entire plane. The error is also smaller than those of the standard AMP and scalar denoising. A practical parameterless version of the GENP-AMP is also developed, which does not need to know the sparsity of the unknown signal and the variance of the GENP. Simulation results are presented to verify the efficiency of the proposed schemes.

Includes members:
  Member "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information")
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block coordinate regularization by denoising")
  Member "binary linear classification and feature selection via generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "an empirical bayes approach to recovering linearly constrained non negative sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning and free energies for vector approximate message passing")
  Member "compressed sensing with prior information optimal strategies geometry and bounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal dictionary learning for image separation with application in art investigation")
  Member "on the convergence of approximate message passing with arbitrary matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional approximate message passing")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  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)