human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information"):
  with members:
    - "an analysis of state evolution for approximate message passing with side information"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "optimal trade offs in multi processor approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "an analysis of state evolution for approximate message passing with side information"):
  with members:
    - "an approximate message passing framework for side information"
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "multi terminal probabilistic compressed sensing"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "exact recovery conditions for sparse representations with partial support information"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "multi resolution compressed sensing via approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"
    - "from denoising to compressed sensing"
    - "spatially scalable compressed image sensing with hybrid transform and inter layer prediction model"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "message passing algorithms for compressed sensing"

Question: Please tell me which class the center node 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)