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 "analysis of mismatched estimation errors using gradients of partition functions"):
  Abstract: We consider the problem of signal estimation (denoising) from a statistical-mechanical perspective, in continuation to a recent work on the analysis of mean-square error (MSE) estimation using a direct relationship between optimum estimation and certain partition functions. The paper consists of essentially two parts. In the first part, using the aforementioned relationship, we derive single-letter expressions of the mismatched MSE of a codeword (from a randomly selected code), corrupted by a Gaussian vector channel. In the second part, we provide several examples to demonstrate phase transitions in the behavior of the MSE. These examples enable us to understand more deeply and to gather intuition regarding the roles of the real and the mismatched probability measures in creating these phase transitions.

Includes members:
  Member "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends")
  Member "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization")
  Member "mmse of bad codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on communication through a gaussian channel with an mmse disturbance constraint")
  Member "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding phase transitions via mutual information and mmse")
  Member "on regret of parametric mismatch in minimum mean square error estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimation in poisson noise properties of the conditional mean estimator")
  Member "mutual information relative entropy and estimation in the poisson channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a class of lower bounds for bayesian risk with a bregman loss")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "universal minimax discrete denoising under channel uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "iterative channel estimation for discrete denoising under channel uncertainty")

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)