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: "multi sparse signal recovery for compressive sensing"
  Abstract: Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal reconstruction performance, we can exploit this multi-sparsity to generate a new convex programming model. The latter is formulated with multiple sparsity constraints in multiple domains and the linear measurement fitting constraint. It improves signal recovery performance by additional a priori information. Since some EMG signals exhibit sparsity both in time and frequency domains, we take them as example in numerical experiments. Results show that the newly proposed method achieves better performance for multi-sparse signals.

Connected through hyperedge HE-1 (induced by source paper "robust compressed sensing under matrix uncertainties"):
  with members:
    - "augmented ell_1 and nuclear norm models with a globally linearly convergent algorithm"
    - "multi structural signal recovery for biomedical compressive sensing"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"

Connected through hyperedge HE-2 (induced by source paper "robust sparse signal recovery for compressed sensing with sampling and dictionary uncertainties"):
  with members:
    - "augmented ell_1 and nuclear norm models with a globally linearly convergent algorithm"
    - "multi structural signal recovery for biomedical compressive sensing"
    - "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"

Connected through hyperedge HE-3 (induced by source paper "multi structural signal recovery for biomedical compressive sensing"):
  with members:
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "matrix completion with noise"
    - "comparing measures of sparsity"

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)