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: "information complexity and estimation"
  Abstract: We consider an input $x$ generated by an unknown stationary ergodic source $X$ that enters a signal processing system $J$, resulting in $w=J(x)$. We observe $w$ through a noisy channel, $y=z(w)$; our goal is to estimate x from $y$, $J$, and knowledge of $f_{Y|W}$. This is universal estimation, because $f_X$ is unknown. We provide a formulation that describes a trade-off between information complexity and noise. Initial theoretical, algorithmic, and experimental evidence is presented in support of our approach.

Connected through hyperedge HE-1 (induced by source paper "approximate message passing with universal denoising"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-2 (induced by source paper "approximate message passing algorithm with universal denoising and gaussian mixture learning"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing via universal denoising and approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "signal estimation with additive error metrics in compressed sensing"
    - "empirical bayes and full bayes for signal estimation"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-4 (induced by source paper "empirical bayes and full bayes for signal estimation"):
  with members:
    - "parameterless optimal approximate message passing"
    - "performance regions in compressed sensing from noisy measurements"
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "recovery from linear measurements with complexity matching universal signal estimation"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "empirical bayes and full bayes for signal estimation"
    - "compressed sensing via universal denoising and approximate message passing"
    - "performance regions in compressed sensing from noisy measurements"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "an mcmc approach to universal lossy compression of analog sources"
    - "an mdl framework for sparse coding and dictionary learning"

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)