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 "statistical mechanics of map estimation general replica ansatz"):
  Abstract: The large-system performance of MAP estimation is studied considering a general distortion function when the observation vector is received through a linear system with additive white Gaussian noise. The analysis considers the system matrix to be chosen from the large class of rotationally invariant random matrices. We take a statistical mechanical approach by introducing a spin glass corresponding to the estimator, and employing the replica method for the large-system analysis. In contrast to earlier replica based studies, our analysis evaluates the general replica ansatz of the corresponding spin glass and determines the asymptotic distortion of the estimator for any structure of the replica correlation matrix. Consequently, the replica symmetric as well as the Replica Symmetry (RS) breaking ansatz with $b$ steps of breaking is deduced from the given general replica ansatz. The generality of our distortion function lets us derive a more general form of the MAP decoupling principle. Based on the general replica ansatz, we show that for any structure of the replica correlation matrix, the vector-valued system decouples into a bank of equivalent decoupled linear systems followed by MAP estimators. The structure of the decoupled linear system is further studied under both the RS and the Replica Symmetry Breaking (RSB) assumptions. For $b$ steps of RSB, the decoupled system is found to be an additive system with a noise term given as the sum of an independent Gaussian random variable with $b$ correlated impairment terms. As an application of our study, we investigate large compressive sensing systems by considering the $\ell_p$ minimization recovery schemes. Our numerical investigations show that the replica symmetric ansatz for $\ell_0$ norm recovery fails to give an accurate approximation of the mean square error as the compression rate grows, and therefore, the RSB ans\"atze are needed.

Includes members:
  Member "glse precoders for massive mimo systems analysis and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robustness of low complexity massive mimo architectures against passive eavesdropping")
  Member "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "asymptotics of nonlinear lse precoders with applications to transmit antenna selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness of low complexity massive mimo architectures against passive eavesdropping")
  Member "replica symmetry breaking in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maximum a posteriori signal recovery with prior information applications to compressive sensing")
  Member "universal sparse superposition codes with spatial coupling and gamp decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "mutual information and optimality of approximate message passing in random linear estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "the replica symmetric prediction for compressed sensing with gaussian matrices is exact":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")

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)