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: "minimum complexity pursuit stability analysis"
  Abstract: A host of problems involve the recovery of structured signals from a dimensionality reduced representation such as a random projection; examples include sparse signals (compressive sensing) and low-rank matrices (matrix completion). Given the wide range of different recovery algorithms developed to date, it is natural to ask whether there exist "universal" algorithms for recovering "structured" signals from their linear projections. We recently answered this question in the affirmative in the noise-free setting. In this paper, we extend our results to the case of noisy measurements.

Connected through hyperedge HE-1 (induced by source paper "universal compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "signal recovery on incoherent manifolds"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "stable image reconstruction using total variation minimization"
    - "sampling and recovery of pulse streams"
    - "optimal phase transitions in compressed sensing"
    - "minimum complexity pursuit"
    - "model based compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "from compression to compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "tight sufficient conditions on exact sparsity pattern recovery"):
  with members:
    - "information theoretic bounds for compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "shannon theoretic limits on noisy compressive sampling"

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)