human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "algorithms for the iterative estimation of discrete valued sparse vectors"
  Abstract: In Compressed Sensing, a real-valued sparse vector has to be estimated from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. For the estimation of these discrete-valued vectors, matched algorithms are required which take the additional knowledge of the discrete nature into account. In this paper, the estimation problem is treated from a communications engineering point of view. A powerful new algorithm incorporating techniques known from digital communications and information theory is derived. For comparison, Turbo Compressed Sensing is adapted to the discrete setup and a simplified and generalized notation is presented. The performance of the algorithms is covered by numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "unveiling bias compensation in turbo based algorithms for discrete compressed sensing"):
  with members:
    - "low complexity iterative algorithms for discrete compressed sensing"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "non linear transformations of gaussians and gaussian mixtures with implications on estimation and information theory"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "low complexity iterative algorithms for discrete compressed sensing"):
  with members:
    - "turbo compressed sensing with partial dft sensing matrix"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Question: Please tell me which class the center node 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)