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 "understanding phase transitions via mutual information and mmse"):
  Abstract: The ability to understand and solve high-dimensional inference problems is essential for modern data science. This article examines high-dimensional inference problems through the lens of information theory and focuses on the standard linear model as a canonical example that is both rich enough to be practically useful and simple enough to be studied rigorously. In particular, this model can exhibit phase transitions where an arbitrarily small change in the model parameters can induce large changes in the quality of estimates. For this model, the performance of optimal inference can be studied using the replica method from statistical physics but, until recently, it was not known if the resulting formulas were actually correct. In this chapter, we present a tutorial description of the standard linear model and its connection to information theory. We also describe the replica prediction for this model and outline the authors' recent proof that it is exact.

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 "multi layer generalized linear estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "generalized expectation consistent signal recovery for nonlinear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "dynamical functional theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a unified framework of state evolution for message passing algorithms")
  Member "phase transitions optimal errors and optimality of message passing in generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the mutual information in random linear estimation beyond i i d matrices")
  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 "expectation consistent approximate inference generalizations and convergence":
    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)