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: "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
  Abstract: As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study proposes and evaluates a spatio-temporal sparse Bayesian learning algorithm, which has the desired ability to recover such non-sparse biosignals. It exploits both temporal correlation in each individual biosignal and inter-channel correlation among biosignals from different channels. The proposed algorithm was used for compressed sensing of multichannel electroencephalographic (EEG) signals for estimating vehicle drivers' drowsiness. Results showed that the drowsiness estimation was almost unaffected even if raw EEG signals (containing various artifacts) were compressed by 90%.

Connected through hyperedge HE-1 (induced by source paper "bayesian de quantization and data compression for low energy physiological signal telemonitoring"):
  with members:
    - "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"
    - "low complexity multiclass encryption by compressed sensing"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "model based compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"):
  with members:
    - "forest sparsity for multi channel compressive sensing"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "compression via compressive sensing a low power framework for the telemonitoring of multi channel physiological signals"):
  with members:
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "efficient recovery of block sparse signals via zero point attracting projection"
    - "fast marginalized block sparse bayesian learning algorithm"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-4 (induced by source paper "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"):
  with members:
    - "efficient recovery of block sparse signals via zero point attracting projection"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"

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)