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 "theoretical links between universal and bayesian compressed sensing algorithms"):
  Abstract: Quantized maximum a posteriori (Q-MAP) is a recently-proposed Bayesian compressed sensing algorithm that, given the source distribution, recovers $X^n$ from its linear measurements $Y^m=AX^n$, where $A\in R^{m\times n}$ denotes the known measurement matrix. On the other hand, Lagrangian minimum entropy pursuit (L-MEP) is a universal compressed sensing algorithm that aims at recovering $X^n$ from its linear measurements $Y^m=AX^n$, without having access to the source distribution. Both Q-MAP and L-MEP provably achieve the minimum required sampling rates, in noiseless cases where such fundamental limits are known. L-MEP is based on minimizing a cost function that consists of a linear combination of the conditional empirical entropy of a potential reconstruction vector and its corresponding measurement error. In this paper, using a first-order linear approximation of the conditional empirical entropy function, L-MEP is connected with Q-MAP. The established connection between L-MEP and Q-MAP leads to variants of Q-MAP which have the same asymptotic performance as Q-MAP in terms of their required sampling rates. Moreover, these variants suggest that Q-MAP is robust to small error in estimating the source distribution. This robustness is theoretically proven and the effect of a non-vanishing estimation error on the required sampling rate is characterized.

Includes members:
  Member "an efficient algorithm for compression based compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "snapshot compressed sensing performance bounds and algorithms")
  Member "on the information dimension of stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the compressibility of affinely singular random vectors")
  Member "compression based compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using black box compression algorithms for phase retrieval")
  Member "new approach to bayesian high dimensional linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solving inverse problems via auto encoders")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "minimum complexity pursuit for universal compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving inverse problems via auto encoders")
  Member "recovery from linear measurements with complexity matching universal signal estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")
  Member "lossy compression of discrete sources via the viterbi algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "new approach to bayesian high dimensional linear regression")

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)