human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compression via compressive sensing a low power framework for the telemonitoring of multi channel physiological signals"):
  Abstract: Telehealth and wearable equipment can deliver personal healthcare and necessary treatment remotely. One major challenge is transmitting large amount of biosignals through wireless networks. The limited battery life calls for low-power data compressors. Compressive Sensing (CS) has proved to be a low-power compressor. In this study, we apply CS on the compression of multichannel biosignals. We firstly develop an efficient CS algorithm from the Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination of the block sparse model and multiple measurement vector model. Experiments on real-life Fetal ECGs showed that the proposed algorithm has high fidelity and efficiency. Implemented in hardware, the proposed algorithm was compared to a Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one has low power consumption and occupies less computational resources.

Includes members:
  Member "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian de quantization and data compression for low energy physiological signal telemonitoring")
  Member "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "framework for a perceptive mobile network using joint communication and radar sensing")
  Member "efficient recovery of block sparse signals via zero point attracting projection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block sparsity induced adaptive filter for multi clustering system identification")
  Member "fast marginalized block sparse bayesian learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scan based compressed terahertz imaging and real time reconstruction via the complex valued fast block sparse bayesian learning algorithm")
  Member "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")

Question: Please tell me which class the source paper 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)