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: "sparse signal separation in redundant dictionaries"
  Abstract: We formulate a unified framework for the separation of signals that are sparse in “morphologically” different redundant dictionaries. This formulation incorporates the so-called “analysis” and “synthesis” approaches as special cases and contains novel hybrid setups. We find corresponding coherence-based recovery guarantees for an l 1 -norm based separation algorithm. Our results recover those reported in Studer and Baraniuk, ACHA, submitted, for the synthesis setting, provide new recovery guarantees for the analysis setting, and form a basis for comparing performance in the analysis and synthesis settings. As an aside our findings complement the D-RIP recovery results reported in Candes et al., ACHA, 2011, for the “analysis” signal recovery problem minimize x  ‖Ψx‖ 1  subject to ‖y − Ax‖ 2  ≤ ∊ by delivering corresponding coherence-based recovery results.

Connected through hyperedge HE-1 (induced by source paper "fast signal separation of 2 d sparse mixture via approximate message passing"):
  with members:
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "recovery of sparsely corrupted signals"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "stable restoration and separation of approximately sparse signals"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "exact recoverability from dense corrupted observations via l_1 minimization"
    - "recovery of sparsely corrupted signals"
    - "model based compressive sensing"
    - "new bounds for restricted isometry constants"

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)