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: "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
  Abstract: Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in Filed Programmable Gate Array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.

Connected through hyperedge HE-1 (induced by source paper "framework for a perceptive mobile network using joint communication and radar sensing"):
  with members:
    - "toward dual functional radar communication systems optimal waveform design"
    - "mu mimo communications with mimo radar from co existence to joint transmission"
    - "framework for an innovative perceptive mobile network using joint communication and sensing"
    - "super resolution delay doppler estimation for ofdm passive radar"
    - "compressed sensing off the grid"

Connected through hyperedge HE-2 (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"
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (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"
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-4 (induced by source paper "compression via compressive sensing a low power framework for the telemonitoring of multi channel physiological signals"):
  with members:
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "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-5 (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"
    - "multi structural signal recovery for biomedical 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)