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 "block sparsity induced adaptive filter for multi clustering system identification"):
  Abstract: In order to improve the performance of least mean square (LMS)-based adaptive filtering for identifying block-sparse systems, a new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed in this paper. The basis of the proposed algorithm is to insert a penalty of block-sparsity, which is a mixed \$l_{2, 0}\$ norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm. To describe a block-sparse system response, we first propose a Markov-Gaussian model, which can generate a kind of system responses of arbitrary average sparsity and arbitrary average block length using given parameters. Then we present theoretical expressions of the steady-state misadjustment and transient convergence behavior of BS-LMS with an appropriate group partition size for white Gaussian input data. Based on the above results, we theoretically demonstrate that BS-LMS has much better convergence behavior than \$l_0\$-LMS with the same small level of misadjustment. Finally, numerical experiments verify that all of the theoretical analysis agrees well with simulation results in a large range of parameters.

Includes members:
  Member "reweighted l1 norm penalized lms for sparse channel estimation and its analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iterative promoting variable step size least mean square algorithm for adaptive sparse channel estimation")
  Member "efficient recovery of block sparse signals via zero point attracting projection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compression via compressive sensing a low power framework for the telemonitoring of multi channel physiological signals")
  Member "performance analysis of l_0 norm constraint least mean square algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "near oracle performance of greedy block sparse estimation techniques from noisy measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved support recovery guarantees for the group lasso with applications to structural health monitoring")
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing")
  Member "sparse recovery using smoothed ell 0 sl0 convergence analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recovery of low rank matrices under affine constraints via a smoothed rank function")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "l_ 0 norm constraint lms algorithm for sparse system identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning")

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)