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: "efficient recovery of block sparse signals via zero point attracting projection"
  Abstract: In this paper, we consider compressed sensing (CS) of block-sparse signals, i.e., sparse signals that have nonzero coefficients occurring in clusters. An efficient algorithm, called zero-point attracting projection (ZAP) algorithm, is extended to the scenario of block CS. The block version of ZAP algorithm employs an approximate $l_{2,0}$ norm as the cost function, and finds its minimum in the solution space via iterations. For block sparse signals, an analysis of the stability of the local minimums of this cost function under the perturbation of noise reveals an advantage of the proposed algorithm over its original non-block version in terms of reconstruction error. Finally, numerical experiments show that the proposed algorithm outperforms other state of the art methods for the block sparse problem in various respects, especially the stability under noise.

Connected through hyperedge HE-1 (induced by source paper "block sparsity induced adaptive filter for multi clustering system identification"):
  with members:
    - "reweighted l1 norm penalized lms for sparse channel estimation and its analysis"
    - "performance analysis of l_0 norm constraint least mean square algorithm"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery using smoothed ell 0 sl0 convergence analysis"
    - "model based compressive sensing"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-2 (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"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "fast marginalized block sparse bayesian learning algorithm"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-3 (induced by source paper "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"):
  with members:
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"

Connected through 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"):
  with members:
    - "fast marginalized block sparse bayesian learning algorithm"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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)