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 structural signal recovery for biomedical compressive sensing"
  Abstract: Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in di?fferent domains, piecewise smoothness, low rank, etc. We propose a framework to exploit all the available structure information. A new convex programming problem is generated with multiple convex structure-inducing constraints and the linear measurement fitting constraint. With additional a priori information for solving the underdetermined system, the signal recovery performance can be improved. In numerical experiments, we compare the proposed method with classical methods. Both simulated data and real-life biomedical data are used. Results show that the newly proposed method achieves better reconstruction accuracy performance in term of both L1 and L2 errors.

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 sparse signal recovery for 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 sparse signal recovery for 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 "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"):
  with members:
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"

Connected through hyperedge HE-4 (induced by source paper "simultaneous greedy analysis pursuit for compressive sensing of multi channel ecg signals"):
  with members:
    - "analysis based sparse reconstruction with synthesis based solvers"

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)