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: "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
  Abstract: Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to nonsparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels. This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.

Connected through hyperedge HE-1 (induced by source paper "rectified gaussian scale mixtures and the sparse non negative least squares problem"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "robust face recognition via block sparse bayesian learning"
    - "expectation maximization gaussian mixture approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-2 (induced by source paper "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"):
  with members:
    - "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"
    - "joint sparsity recovery for spectral compressed sensing"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"):
  with members:
    - "multi structural signal recovery for biomedical compressive sensing"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"

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)