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: "distributed diffusion based lms for node specific adaptive parameter estimation"
  Abstract: A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest, parameters of common interest to a subset of nodes and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.

Connected through hyperedge HE-1 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part ii performance analysis"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "network lasso clustering and optimization in large graphs"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "multitask diffusion adaptation over networks"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "learning over multitask graphs part i stability analysis"):
  with members:
    - "learning over multitask graphs part ii performance analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "signals on graphs uncertainty principle and sampling"
    - "network lasso clustering and optimization in large graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-3 (induced by source paper "learning over multitask graphs part ii performance analysis"):
  with members:
    - "learning over multitask graphs part i stability analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "diffusion lms over multitask networks"
    - "network lasso clustering and optimization in large graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-4 (induced by source paper "online distributed learning over networks in rkh spaces using random fourier features"):
  with members:
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "efficient klms and krls algorithms a random fourier feature perspective"
    - "on the error of random fourier features"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-5 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation"):
  with members:
    - "distributed local linear parameter estimation using gaussian spawn"
    - "robust decentralized detection and social learning in tandem networks"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed localization of a rf target in nlos environments"
    - "multitask diffusion adaptation over asynchronous networks"
    - "diffusion lms for clustered multitask networks"

Connected through hyperedge HE-6 (induced by source paper "multitask diffusion adaptation over networks with common latent representations"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-7 (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"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "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-8 (induced by source paper "proximal multitask learning over networks with sparsity inducing coregularization"):
  with members:
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion lms for clustered multitask networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"

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.CY(Computers and Society)