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 "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"):
  Abstract: Signal recovery from unitarily invariant measurements is investigated in this paper. A message-passing algorithm is formulated on the basis of expectation propagation (EP). A rigorous analysis is presented for the dynamics of the algorithm in the large system limit, where both input and output dimensions tend to infinity while the compression rate is kept constant. The main result is the justification of state evolution (SE) equations conjectured by Ma and Ping. This result implies that the EP-based algorithm achieves the Bayes-optimal performance that was originally derived via a non-rigorous tool in statistical physics and proved partially in a recent paper, when the compression rate is larger than a threshold. The proof is based on an extension of a conventional conditioning technique for the standard Gaussian matrix to the case of the Haar matrix.

Includes members:
  Member "the mutual information in random linear estimation beyond i i d matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overlap matrix concentration in optimal bayesian inference")
  Member "the replica symmetric prediction for compressed sensing with gaussian matrices is exact":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "expectation consistent approximate inference generalizations and convergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "the mutual information in random linear estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective")
  Member "performance improvement of iterative multiuser detection for large sparsely spread cdma systems by spatial coupling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a generalization of threshold saturation application to spatially coupled bicm id")
  Member "turbo compressed sensing with partial dft sensing matrix":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "s amp approximate message passing for general matrix ensembles":
    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)