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 "the mutual information in random linear estimation beyond i i d matrices"):
  Abstract: There has been definite progress recently in proving the variational single-letter formula given by the heuristic replica method for various estimation problems. In particular, the replica formula for the mutual information in the case of noisy linear estimation with random i.i.d. matrices, a problem with applications ranging from compressed sensing to statistics, has been proven rigorously. In this contribution we go beyond the restrictive i.i.d. matrix assumption and discuss the formula proposed by Takeda, Uda, Kabashima and later by Tulino, Verdu, Caire and Shamai who used the replica method. Using the recently introduced adaptive interpolation method and random matrix theory, we prove this formula for a relevant large sub-class of rotationally invariant matrices.

Includes members:
  Member "optimal errors and phase transitions in high dimensional generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entropy and mutual information in models of deep neural networks")
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "the layered structure of tensor estimation and its mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "multi layer generalized linear estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "additivity of information in multilayer networks via additive gaussian noise transforms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "phase transitions optimal errors and optimality of message passing in generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding phase transitions via mutual information and mmse")

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)