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 "asymptotic analysis of complex lasso via complex approximate message passing camp"):
  Abstract: Recovering a sparse signal from an undersampled set of random linear measurements is the main problem of interest in compressed sensing. In this paper, we consider the case where both the signal and the measurements are complex. We study the popular reconstruction method of $\ell_1$-regularized least squares or LASSO. While several studies have shown that the LASSO algorithm offers desirable solutions under certain conditions, the precise asymptotic performance of this algorithm in the complex setting is not yet known. In this paper, we extend the approximate message passing (AMP) algorithm to the complex signals and measurements and obtain the complex approximate message passing algorithm (CAMP). We then generalize the state evolution framework recently introduced for the analysis of AMP, to the complex setting. Using the state evolution, we derive accurate formulas for the phase transition and noise sensitivity of both LASSO and CAMP.

Includes members:
  Member "expectation maximization gaussian mixture approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "on phase transition of compressed sensing in the complex domain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff")
  Member "approximate message passing with consistent parameter estimation and applications to sparse learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization")
  Member "compressed sensing over ell_p balls minimax mean square error":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "simple bounds for noisy linear inverse problems with exact side information")
  Member "a message passing receiver for bicm ofdm over unknown clustered sparse channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity optimality of amp in coded systems")
  Member "minimum complexity pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)