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: "signal reconstruction in linear mixing systems with different error metrics"
  Abstract: We consider the problem of reconstructing a signal from noisy measurements in linear mixing systems. The reconstruction performance is usually quantified by standard error metrics such as squared error, whereas we consider any additive error metric. Under the assumption that relaxed belief propagation (BP) can compute the posterior in the large system limit, we propose a simple, fast, and highly general algorithm that reconstructs the signal by minimizing the user-defined error metric. For two example metrics, we provide performance analysis and convincing numerical results. Finally, our algorithm can be adjusted to minimize the $\ell_\infty$ error, which is not additive. Interestingly, $\ell_{\infty}$ minimization only requires to apply a Wiener filter to the output of relaxed BP.

Connected through hyperedge HE-1 (induced by source paper "signal estimation with additive error metrics in compressed sensing"):
  with members:
    - "hybrid approximate message passing"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "hybrid approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "hybrid approximate message passing with applications to structured sparsity"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "wiener filters in gaussian mixture signal estimation with infinity norm error"):
  with members:
    - "signal estimation with additive error metrics in compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "analysis of belief propagation for non linear problems the example of cdma or how to prove tanaka s formula"
    - "randomly spread cdma asymptotics via statistical physics"

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)