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: "sparse distributed learning based on diffusion adaptation"
  Abstract: This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery.

Connected through hyperedge HE-1 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery"):
  with members:
    - "next in network nonconvex optimization"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "distributed compressed sensing for static and time varying networks"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive diffusion schemes for heterogeneous networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "diffusion leaky lms algorithm analysis and implementation"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "diffusion adaptation over multi agent networks with wireless link impairments"
    - "a novel normalized sign algorithm for system identification under impulsive noise interference"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed decision making over adaptive networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "analysis of a reduced communication diffusion lms algorithm"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-4 (induced by source paper "diffusion adaptation framework for compressive sensing reconstruction"):
  with members:
    - "adaptive diffusion schemes for heterogeneous networks"
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "diffusion adaptation over networks"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "diffusion l0 norm constraint improved proportionate lms algorithm for sparse distributed estimation"):
  with members:
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-6 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-7 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-8 (induced by source paper "least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"

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.LG(Machine Learning)